The measurement values of this study
\r\n\tThis book will discuss the mechanisms by which TTM can mitigate the pathophysiologies responsible for secondary brain injury, as well as the available evidence for use of TTM in multiple neurologic injuries (stated above). In addition, this review will also provide information to help guide this treatment with regard to timing, depth, duration, and management of side-effects. It will also address normothermia and fever prevention in brain injury.
\r\n\tThe book will also discuss the pathophysiology and therapeutic approach to shivering during TTM. It will also provide grounds for future directions in the application of and research with TTM.
This chapter aims to review the dye-sensitized solar cells (DSSCs) with graphene structure. DSSCs have been under extensive research. Since the color of the device can be easily varied by choosing different dyes and cells on flexible substrates have been already demonstrated, DSSCs are especially attractive for building integrated photovoltaics. The cell concept can reduce the production costs and energy payback time significantly compared to standard silicon cells or other thin film cells.
However, one of the major issues hindering the rapid commercialization of DSSCs is their lower conversion efficiency compared to conventional p-n junction solar cells [1]. That may be attributed to poor charge separation in DSSC structure. Therefore, charge transfer structure, such as Au nanoparticles and quantum dots, has been employed in a DSSC to improve the device performance through charge separation in the photoelectrodes [2-5].
Graphene is a potential material for many applications due to their high electron mobility, outstanding optical properties, and thermal, chemical, and mechanical stability [6-10]. Therefore, the second section in this article illustrates the principle of electron extraction layer. TiO2 plays an important role on the electron-extraction layer. We will discuss the electron transmission on dye-sensitized solar cells. The effect of the electron-transporting layer of the solar cell is very important. Therefore, we show the I-V characteristics of the DSSCs. The cell performance was measured, which had different electron-extraction layer structures.
The third section discusses the preparation method of the graphene. Graphene is a potential material for many applications due to their high electron mobility, outstanding optical properties, and thermal, chemical, and mechanical stability.
In the fourth section, the graphene was introduced into the DSSC structure to improve electron conversion efficiency. This study investigates the effect on the graphene layer as electron transport layer in the DSSC structure deposited by the magnetron sputtering method; in particular, it examines the performance of the DSSCs with the graphene electron transport layer.
The fifth section reveals a new DSSC structure. The structure has provided excellent performance and higher photoelectric conversion efficiency by DSSC with the TiO2/graphene/TiO2 sandwich structure. This section focuses on the improvement that is associated with the increase in electron transport efficiency and the absorption of light in the visible range.
The concluding paragraphs will summarize some parameters of DSSC with or without a graphene layer which was prepared by sputtering and then discuss the DSSC’s parameters and reasons which have different preparation methods of graphene layer. Finally, there are some concluding remarks.
Figure 1 sketches the structure of the basic DSSC which can be divided into several parts. They have a basic structure that comprises two conductive substrates (one is photoelectrode and the other is counter electrode), an absorbing layer of semiconductor materials, dye molecules, and a redox electrolyte. The basic principle of operation of DSSCs includes the following: (1) the light irradiates on the DSSC and the photons will pass through the photoelectrode to the dye layer which is absorbed by the photosensitizer dye molecule. (2) The photosensitizers are excited from the ground state (S) to the excited state (S∗). The excited electrons are injected into the conduction band of the TiO2 electrode. This results in the oxidation of the photosensitizer (S+). (3) Electrons are injected from the photoexcited dye into the conductive band of the semiconductor. The electrons will pass from the electric transport layer to the external circuit. (4) The oxidized photosensitizer (S+) accepts electrons from the I− ion redox mediator, leading to regeneration of the ground state (S), and the I− is oxidized to the oxidized state, I3−, and transports the positive charges to the counter electrode. (5) The oxidized redox mediator, I3−, diffuses toward the counter electrode and then it is reduced to I− ions.
Schematic cross section of the completed structure
The principle of electron transport (or extraction) layer inserted in the traditional DSSC structure had been reported [11-17]. Figure 2 shows the energy level diagram and mechanism of photocurrent generation in TiO2 DSSCs with the graphene layer. The work function of the graphene layer is around 4.5 eV [18,19]. Graphene has a work function similar to that of the indium tin oxide (ITO) (4.8 eV) electrode. The graphene layer does not prevent the flow of injected electrons down to the ITO electrode because its work function exceeds that of the ITO electrode [20-22]. Therefore, the brief operating process is as follows. Dye N719 was excited by incident light, and electrons transit from HOMO to LUMO. The LUMO and HOMO are the lowest unoccupied molecular orbit and highest occupied molecular orbit, respectively. Electrons are injected into the graphene electron transport layer via the TiO2 photoelectrode. The electrons transferred to the graphene electron transport layer were collected at the back contact to generate a photocurrent. Therefore, the inserted graphene layer collects electrons and acts as a transporter in the effective separation of charge and rapid transport of the photogenerated electrons.
Energy level diagram and mechanism of photocurrent generation in the DSSCs with the graphene electron transfer layer
Graphene is a potential material for many applications such as extensively utilized in organic photovoltaic (PV) cells. It has excellent optical and electrical characteristics which are exploited in transparent conductive films or electrodes by their high electron mobility [6-8,10,17], outstanding optical properties, and thermal, chemical, and mechanical stability [6–10,12]. However, it is hard to produce high-quality graphene to use in the sputter deposition method. Therefore, this study uses plating method to plate graphene and compare with the traditional method.
First, acetylacetone and Triton X-100 were added into 10 ml water by using syringe. The TiO2 compound solution was stirred for 24 h using a magnetic stirrer. After mixing the TiO2 compound solution, the TiO2 colloid is obtained. The graphene was stacked on the ITO substrate by electroplating process. The plating solution is graphene dispersion. The plating solution was injected into the beaker and stirred with an air pump. Figure 3 shows the electroplating process. The anode connected to the graphite, and the cathode connected to the ITO substrate. The speeds of the coating process were 500 rpm for 20 s and 2,000 rpm for 60 s. The thickness of TiO2 is about 13 μm. After the annealing process by using RTA (rapid thermal annealing) at 450 oC for 30 min, the strength of the anatase structure would be enhanced. When the samples cool down to room temperature, they were soaked into the N719 solution; the N719 solution is mixed ethanol and N719 powder. The samples will produce electrons when they are illuminated with light after they are soaked into the N719 solution.
The graphene electroplating methods
A graphene layer was sputtered on indium tin oxide (ITO) conductive glass substrate by radio-frequency magnetron sputtering with a graphite target. It is the electron transport layer that improves the electron transfer in the DSSC structure.
First, the solution consisting of TiO2 nanocrystalline powder, Triton X-100, acetic acid, and deionized water was mixed as a colloidal solution, and the colloidal solutions were daubed uniformly onto the graphene electron transfer layer to form a thick film. After annealing, the photoelectrode with the graphene layer was immersed in N719 dye absorption ((Bu4N)2-[Ru(dcbpyH)2(NCS)2] complex) in ethanol for 24 h. To increase its anatase content, the samples were sintered at 450 for 30 min. The electrolyte was composed of iodide and lithium iodide with and without 4-tertbutylpyridine (TBP) in propylene carbonate. Then a thick layer of platinum was sputtered onto ITO substrate as a counter electrode. Cells were fabricated by placing sealing films (SX1170-60, Solaronix) between the two electrodes and leaving just two via-holes for injection of electrolyte. The sealing process was carried out on a hot plate. Then the electrolyte was injected into the space between the two electrodes through the via-holes. Finally, the via-holes were sealed using epoxy with low vapor transmission rate.
This study fabricated three different samples: samples A and B are plated with graphene for 20 min and 30 min, respectively, and sample C is a normal DSSC. Figure 4 and Table 1 show the I-V curves and the measurement values. The cell is measured under AM 1.5 illumination at 25 oC. The active area is 0.3×0.3 cm2. The short-circuit current densities of the samples are 4.97 mA/cm2 (electroplated with graphene for 20 min), 5.42 mA/cm2 (electroplated with graphene for 30 min), and 11.2 mA/cm2 (normal DSSC), respectively. The value of open-circuit voltage between the samples only has a slight difference. The efficiency of the samples are 0.796 % (electroplated with graphene for 20 min), 0.844 % (electroplated with graphene for 30 min) and 3.93 % (normal DSSC), respectively.
I-V curves of three samples
\n\t\t\t\tSample\n\t\t\t | \n\t\t\t\n\t\t\t\tJsc (mA/cm2)\n\t\t\t | \n\t\t\t\n\t\t\t\tVoc (mV)\n\t\t\t | \n\t\t\t\n\t\t\t\tFF%\n\t\t\t | \n\t\t\t\n\t\t\t\tEfficiency (%)\n\t\t\t | \n\t\t
\n\t\t\t\tA(20 min)\n\t\t\t | \n\t\t\t4.97 | \n\t\t\t0.4 | \n\t\t\t0.401 % | \n\t\t\t0.796 % | \n\t\t
\n\t\t\t\tB(30 min)\n\t\t\t | \n\t\t\t5.42 | \n\t\t\t0.4 | \n\t\t\t0.389 % | \n\t\t\t0.844 % | \n\t\t
\n\t\t\t\tC (normal DSSC)\n\t\t\t | \n\t\t\t11.2 | \n\t\t\t0.6 | \n\t\t\t0.585 % | \n\t\t\t3.93 % | \n\t\t
The measurement values of this study
The Raman spectra of plated graphene and sputtered graphene in G-band and D-band
The Raman spectra of plated graphene and sputtered graphene in 2D-band
Figures 5 and 6 show the Raman spectra of electroplated graphene and sputtered graphene (normal DSSC). As shown in Figure 5, electroplated graphene and sputtered graphene have Raman peaks at 1,350 cm-1 and 1,580 cm-1 [23–25]. The ID/IG of plating graphene is 0.52, and the sputtered graphene is 0.96. The higher value of ID/IG shows good preservation of the highly crystalline structure of graphene. Figure 5 compares the D-band of two different procedures [26]; it shows that using the plating process is much better than using the sputtering process. The G-band is a doubly degenerate phonon mode at the Brillouin zone center; the D-band is a defect and the phonon branches around K point [27]. As shown in Figure 6, the 2D-band is a two-phonon double-resonance process [28] and is similar to the G-band but has a more complicated peak structure [29-31]. It depends on the photon energy and polarization.
Figure 7 shows the top-view SEM image of electroplating graphene on ITO glass. As shown in Figure 7, the graphene flakes with 10 μm width were stacked on the ITO glasses. Figure 8 shows the cross-sectional SEM image of plating graphene on the ITO glass. The graphene is successfully plated on the ITO glasses, and the thickness is around 8 μm.
Top view of electroplating graphene on ITO glass surface
Cross-section SEM image of the plated graphene
Because graphene has high electron mobility, we use graphene as an electron transport layer to improve the electron transfer in the DSSC. That is DSSCs with graphene/TiO2 active layer. The graphene flakes prepared by using the electroplating method have demonstrated a superior graphene property by Raman scattering. However, the DSSCs with graphene flakes exhibited poor power conversion efficiency, owing to the high series resistance caused by the discontinue graphene flakes. Therefore, sputtered graphene was employed to replace the graphene flakes prepared by electroplating to improve the electrical properties of the DSSCs even the sputtered graphene including graphene oxide.
First of all, a 60-nm-thick graphene layer was sputtered on indium tin oxide (ITO) conductive glass substrate by radio-frequency magnetron sputtering as an electron transport layer. Next, the solution consisting of TiO2 was mixed as a colloidal solution which was daubed uniformly onto the graphene electron transfer layer to form a thick film. Then a 100-nm-thick layer of platinum was sputtered onto ITO substrate as a counter electrode. Cells were fabricated by placing sealing films between the two electrodes and leaving just two via-holes for injection of electrolyte. Then, the electrolyte was injected into the space between the two electrodes through the via-holes. Finally, the via-holes were sealed using epoxy with low vapor transmission rate. Figure 9 shows the cross section of the completed structure.
Schematic cross section of the completed structure
Afterward, we began examining its results by comparing the 60-nm-thick graphene electron transport layer with the 100-nm-thick graphene layer. Figure 10 shows the absorption of TiO2 DSSCs with and without the graphene electron transfer layer in visible range. As shown in Figure 10, the graphene electron transport layer has an increased absorption coefficient in the range of 310–400 nm. Therefore, the graphene electron transport layer is also an absorption layer to improve the absorption of the solar cells.
Absorption spectra of the DSSCs with and without the graphene electron transfer layer [12]
I-V curves of the DSSCs with and without the graphene electron transfer layer under illumination [12]
Figure 11 shows the I-V characteristics of the DSSCs. This figure shows cell performance between TiO2 DSSCs and TiO2/graphene under AM 1.5 illumination with a solar intensity of 100 mW/cm2 at 25°C. The cell has an active area of 3×3 mm2 and no antireflective coating.
Finally, we examine its result by measuring the cell parameters, open-circuit voltage (Voc), short-circuit current (Jsc), fill factor (FF), and energy conversion efficiency (Eff) which are summarized in Table 2 [12].
\n\t\t\t | \n\t\t\t\tTiO2\n\t\t\t\t\n\t\t\t | \n\t\t\t\n\t\t\t\tGraphene+TiO2\n\t\t\t\t\n\t\t\t | \n\t\t
\n\t\t\t\tJsc (mA/cm2)\n\t\t\t | \n\t\t\t6.9 | \n\t\t\t17.5 | \n\t\t
\n\t\t\t\tVoc (V)\n\t\t\t | \n\t\t\t0.5 | \n\t\t\t0.5 | \n\t\t
\n\t\t\t\tFF\n\t\t\t | \n\t\t\t0.419 | \n\t\t\t0.456 | \n\t\t
\n\t\t\t\tη (%)\n\t\t\t | \n\t\t\t1.45 | \n\t\t\t3.98 | \n\t\t
The parameters of TiO2 DSSCs with and without graphene electron transport layer [12]
According to Figures 10 and 11 and Table 2, the short-circuit current rises up to 17.5, fill factor to 0.456, and energy conversion efficiency to 3.98 %. The enhanced performance of DSSCs with a graphene was attributed to the increase in electron transport efficiency and light absorption in visible range.
Because of the TiO2/graphene sandwich structure, the efficiency on traditional DSSCs improved. As a result, we use three sandwich structures to achieve the desired outcomes of the following experiment. The enhanced performance of DSSCs with the sandwich structure can be attributed to an increase in electron transport efficiency and in the absorption of light in the visible range. The preparation of TiO2 photoelectrodes is done by the following: the TiO2 slurry was prepared by mixing 6 g of nanocrystalline powder, 0.1 mL Triton X-100, and 0.2 mL acetylacetone. The graphene film is deposited on the surface of the first photoelectrode layer, a single TiO2 photoelectrode layer. This is spin-coated with the rate of rotation of 2,000 rpm, a sandwich structure with three rotational speed to 4,000 rpm, in the present experiment for comparison.
In summary, the DSSC with the sandwich structure in this study exhibited a Voc of 0.6 V, a high Jsc of 11.22 mA cm−2, a fill factor (FF) of 0.58, and a calculated η of 3.93 %, which is 60 % higher than that of a DSSC with the traditional structure.
Figure 12(a) shows the top-view SEM image of the TiO2 nanoparticles with mean diameter of 50 nm. Figure 12(b) shows the cross-sectional SEM image of a TiO2/graphene/TiO2 sandwich structure. The thickness of the graphene electron extraction layer is around 60 nm.
SEM images of (a) TiO2 nanoparticles and (b) TiO2/graphene/TiO2 sandwich structure [17]
Figures 13(a) and 13(b) present the Raman scattering spectra of the graphene film that was deposited on the glass substrate using the process that was described in the section on the preparation of graphene. The spectra include important peaks that correspond to the D-band (approximately 1,350 cm−1), the G-band (approximately 1,580 cm−1), and the 2D-band (approximately 2,700 cm−1).
Figure 14 displays the UV-vis spectra of photoelectrodes with different structures before and after they were loaded with dye. Clearly, the photoelectrode with the TiO2/graphene/TiO2 sandwich structure has a higher absorption than those with the traditional structure both before and after loading with dye.
Figure 15 presents the energy level diagram of the DSSC with the TiO2/graphene/TiO2 sandwich structure. Under illumination, electrons from the photoexcited dye are transported to the conduction band (CB) of TiO2 via the CB of the graphene and TiO2. The transportation path via the CB of graphene is in addition to the traditional path. Owing to the excellent electrical conduction of the graphene, the graphene layer bridges behave as a channel for transferring electrons and rapidly transport the photoexcited electrons. The graphene is homogeneous throughout the system, and the excited electrons are captured by the graphene without any obstruction. The collected electrons can be rapidly and effectively transported to the CB of TiO2 through graphene bridges. In the interface of graphene and TiO2, the resistance through which charges are transported is reduced relative to the DSSC without graphene electron transport layer, and the recombination and back-reaction processes are suppressed.
Raman scattering spectra of graphene film deposited on glass substrate. The spectra include important peaks that correspond to the D-band (1,350 cm−1), the G-band (1,580 cm−1), and the 2D-band (2,700 cm−1) [17]
UV-vis absorption spectra of DSSCs with different structures (a) before and (b) after dye loading [17]
Energy level diagram and mechanism of photocurrent generation in DSSCs with TiO2 /graphene/TiO2 sandwich structure [17]
To enhance the performance of DSSCs, this work used nanostructure graphene electron transfer layer by plating or sputtering and compared the difference between the DSSC structure with graphene/TiO2 and with TiO2/graphene/TiO2. From the I-V curves, sputtered graphene is much better than plated graphene because the plated graphene has a scattered distribution of ITO. The enhanced performance of DSSCs with a graphene may be attributed to the increase in electron transport efficiency and light absorption in visible range, especially in the range of 310–400 nm. Therefore, the efficiency of conversion of solar energy with graphene+TiO2 to electricity were increased from 1.45 % to 3.98 %, and the efficiency of conversion of solar energy with TiO2/graphene/TiO2 sandwich structure to electricity was increased from 1.38 % to 3.93 %, respectively, under simulated full-sun illumination. This improvement in performance is associated with an increase in the absorption of light, a wide range of absorption wavelengths, shorter charge transportation distances, and the suppression of charge recombination when the graphene is applied.
The increasing performance of computers over the last decade has stimulated the development of general-purpose computer vision algorithms. One of the major problems of computer vision is object recognition tasks, to which special attention is paid. This is due to the desire to create artificial intelligent systems. The first step toward any kind of intelligence is perception, followed by reasoning and action.
\nHuman perception is based on visual perception. Since intelligent artificial systems are primarily inspired by human perception and reasoning, we can conclude that visual perception is an important source of information for many potential systems.
\nRecently, there was a raising interest on eye tracking technology. This is mainly due to the industrial growth of many domains such as augmented reality, smart cars, and web applications’ testing for which a solid eye tracking technology is essential. Eye movement recognition, combined with other biometrics such as sound recognition, can enable a smooth interaction with virtual environments.
\nA good example of a smart system is the autonomous car. It perceives the surrounding world and the signs while adapting her behavior to changing situations. Such a car contains a lot of different sensors, which help to perceive the necessary information. The visual perception of the surrounding world is among the most important. It could be used to recognize pedestrians on the street, cars, animals, or even unspecified objects on the road, which could pose a potential threat to human life.
\nImproving and developing object recognition algorithms will help improve not only artificial intelligent systems but many other useful applications in today’s world. Other examples of application of this system can be extended to the tourist industry where applications of augmented reality (Figure 1) are becoming more and more popular especially after the widespread use of smartphones. In addition, the field of video surveillance is also a possible extension of object detection algorithms because of the need for quick and timely detection of different video scenes captured by cameras.
\nAugmented reality.
Indeed, scene comprehension includes many separable tasks ranging from object recognition to the categorization of scenes and events. Object detection is a complex discipline that can be divided into three main directions:
Image classification: The search for images in the majority of search engines is a typical case of image classification algorithms.
Object detection: The location of the object on the request is one of the desired information in many of the systems mentioned.
Segmentation of objects: Which pixels belong to which objects? It is more precisely compared to object detection for obvious reasons.
Object detection is a difficult task mainly because of possible changes in the appearance of the object due to different consequences. The design of the potential method must consider the possible difficulties:
Intra-class variations: An object type can have a large number of variations (Figure 2 illustrates different types of chairs). This can pose a problem by using specific features, which do not cover all possible object variations.
Luminance conditions: Variations in luminance conditions change the appearance of the object, mainly in color and reflection.
Point of view: The majority of objects in the images are in three dimensions. The images are only two dimensions, which means that we can only see a particular view of the given object. The same object may differ from other points of view, which we must also be aware of. Different views of the same object cause the invisibility of different features. Not all features are visible from a single point of view.
Scale: The size of the object may differ, and there is a desire to be able to detect what the object gives to any size or scale.
Location: It is much easier if you know where the desired object is. The situation differs if you have a prior knowledge of the location of the object and no information about it or if you know that the image contains only one object, located in the center of the image.
Orientation: Humans do not have obvious problems recognizing the same object with a different orientation, but many algorithms do. Invariance to this possibility is often crucial.
Occlusion and truncation: Occlusion from one object to another often causes a lot of inconvenience. Sometimes even humans do not see enough features of the object to recognize correctly. Truncation is the same problem when you do not see certain parts of the object, because they are out of the picture.
Footprint: The background is almost nonexistent; an image contains only one chair. This situation is not typical in a real world. When you take the image of an object, there are almost always many other objects in the background, in which the recognition algorithm is usually not interested. The scene is often very complex, and it is difficult to recognize the object/objects desired among other objects.
Out of context: Context is often used to increase the likelihood of certain categories of common occurrences. For example, cars and roads are often associated, but we cannot rely too much on context because sometimes it can be misleading.
Multiple instances: The image often contains several objects of the same category. Some algorithms can identify regions of different categories in the image, but they cannot identify individual instances of the same object category.
Pose: One of the biggest challenges is the invariant detection of the pose. Many objects change their appearance by changing their shape. For example, it is desirable to detect a person in any posture of their body [1].
Instance level recognition vs. object class recognition. It is necessary to realize the difference between these problems. It is obvious that different methods are needed to recognize the human face in general and the person who uses the face.
Image search results.
After analyzing the potential problems associated with recognition tasks, we believe that the direction to follow in imitating the human visual perception system is natural. The first moment of human comprehension of the image is a very general activity that analyzes the basic categories (buildings, men, cars, etc.). After getting the big picture, his attention focuses on the things that interest him. While focusing, humans observe objects of interest to enrich more details and see and recognize more features. A feature is a general term for describing a particular part of the object in order to enrich its appearance. A human has special predispositions on several objects (e.g., faces) and on situations (mainly of the danger and movement type) on which he is more sensitive to recognize. The typical situation is when you see someone away from you and you can recognize that it is a person. As you get closer, by focusing on this person, you are enriching and recognizing more and more elements that make it possible to distinguish whether he is a known person and to detect his name. Humans can do instance-level recognition as in the case presented, but they must first distinguish the object category to optimize the subsequent search.
\nBiometrics has been a concern for centuries. Proving one’s identity reliably was done using several techniques. From prehistory man knew the uniqueness of fingerprints, which meant that signatures by fingerprints were sufficient to prove the identity of an individual. Indeed, two centuries before Christ, the Emperor Ts-In-She authenticated certain sealed with the fingerprint.
\nAt the beginning of the nineteenth century, in France, Alphonse Bertillon launched the first steps of the scientific police. He proposed the first method of biometrics that can be described as a scientific approach: bertillonage allowed the identification of criminals through several physiological measures.
\nAt the beginning of the twentieth century, biometry was rediscovered by William James Herschel, an English officer who had the idea of having his subcontractors sign their fingerprints to find them easily in case of unhonored contracts. As a result, police departments have begun using fingerprints as a unique and reliable feature to identify an individual.
\nBiometrics is constantly growing especially in the field of secure identity documents such as the national identity card, passport, or driving license. This technology is running on new platforms, including chip cards based on the microprocessor.
\nThe biometric market has undergone a great development thanks to the great number of advancement and innovation that this field has experienced in recent decades. This development is increasing as a result of the security concerns of several countries, which has pushed investment in this area and the widespread use of biometric solutions in several social and legal fields.
\nAs shown by the statistics in Figure 3 between 2007 and 2015, there has been a considerable increase in the share of the private sector market due to the growing need for biometric solutions in this sector especially for smartphone and camera manufacturers.
\nDistribution of the global biometric market.
According to ABI Research [2], the global biometric market will break the $30 billion mark by 2021, 118% higher than the 2015 market. In this context, consumer electronics, and smartphones in particular, are boosting the biometric sector: it is expected to sell two billion onboard fingerprint sensors in 2021, for an average annual increase of 40% in 5 years.
\nA biometric system is a system that allows the recognition of a certain characteristic of an individual using mathematical algorithms and biometric data. There are several uses of biometric systems. There are systems that require enrollment upstream of users. Other identification systems do not require this phase.
Enrollment mode is a learning phase that aims to collect biometric information about who to identify. Several data acquisition campaigns can be carried out to ensure a certain robustness of the recognition system to temporal variations of the data. During this phase, the biometric characteristics of individuals are captured by a biometric sensor, and then represented in digital form (signatures), and finally stored in the database. The processing related to the enrollment has no time constraint, since it is performed “off-line.”
The verification or authentication mode is a “one-to-one” comparison, in which the system validates the identity of a person by comparing the biometric data entered with the biometric template of that person stored in the system’s database. In such a mode, the system must then answer the question related to the identity of the user. Currently the verification is carried out via a personal identification number, a user name, or a smart card.
The identification mode is a “one-to-N” comparison, in which the system recognizes an individual by matching it with one of the models in the database. The person may not be in the database. This mode consists of associating an identity with a person.
\nFigure 4 presents the architecture of a biometric system, which consists of the following elements:
The capture module that represents the entry point of the biometric system and consists in acquiring the biometric data in order to extract a digital representation. This representation is used later in the following phases.
The module of signal processing makes it possible to optimize the processing time and the digital representation acquired in the enrollment phase in order to optimize the processing time of the verification phase and the identification.
The storage module that contains the biometric templates of the system enrollees.
The matching module that compares the data extracted by the extraction module with the data of the registered models and determines the degree of similarity between the two biometric data.
The decision module that determines whether the similarity index returns through the matching module is sufficient to make a decision about the identity of an individual.
Biometric system architecture.
For the evaluation of the precision of a biometric system, which makes it possible to measure these performances, numerous attempts have been made on the system, and all the similarity scores are saved.
\nBy applying the variable score threshold to similarity scores, the pairs of false recognition rate (FRR) and false acceptance rate (FAR) can be calculated. The false recognition rate, or FRR, is the measure of the likelihood that the biometric system will incorrectly reject an access attempt by an authorized user. It is stated as the ratio of the number of false recognitions divided by the number of identification attempts. On the other hand, the false acceptance rate, or FAR, is the measure of the likelihood that the biometric system will incorrectly accept an access attempt by an unauthorized user. It is stated as the ratio of the number of false acceptances divided by the number of identification attempts.
\nThe results are presented either as such pairs, i.e., FRR at a certain level of FAR or as the graph in Figure 5. The rates can be expressed in several ways, for example, in percentages (1%), in fractions (1/100), in decimal format (0.01), or using powers of ten (\n
DET graph sample.
There are three modes of performance evaluations, which are technology, scenario, and operational evaluation. When evaluating biometric algorithms, technological evaluations are the most common and often the most feasible. Since this type of evaluation is done using saved samples, the results are reproducible, and the evaluation is not a tedious or complicated process.
Technological evaluation: Evaluation using recorded data, e.g., previously acquired fingerprints
Scenario evaluation: End-to-end evaluation of the system using a prototype or simulated environment
Operational evaluation: Evaluation in which the performance of a complete biometric system is determined in an application environment with a specific population
The biggest disadvantage of technological evaluations is that they do not necessarily reflect the final conditions of use of the system. For this reason, it is important to collect a set of samples of the conditions of use of the target system when preparing an assessment.
\nRegistered samples used in technology assessments are collected in databases. Data collection is performed using a group of volunteers, at least some of whom provide multiple acquisitions of the same biometric modality (e.g., the same finger) to have relevant attempts. To make collection efficient, samples of several objects can be collected from each volunteer, for example, every ten fingers. The characteristics of the database have a great impact on the results of an evaluation. As previously stated, with the exception of the capabilities of the biometric algorithm, the amount of available information can be used to characterize the objects.
\nTo be able to make an assertion about the FRR 1% @ FAR 1 / 1 000 000 (i.e., when the system operates in a mode where one out of one million impostor attempts is-falsely-considered a match, one percent of the genuine attempts would fail) it at least one million impostor attempts (user sticking perfectly to another person’s template). It is not difficult to understand that the uncertainty of such an assertion would be rather high. The result depends heavily on how the two most similar samples in the database are scored. When comparing and viewing a DET (detection error trade-off) graph, it is important to understand that the uncertainty is higher on the side of the edges of the image. The number of comparisons made is only an important factor affecting confidence. The key to getting better statistical significance is to make as many uncorrelated attempts as possible.
\nBiometric systems can be used in a large number of applications. For security reasons, biometrics can help make transactions, and everyday life is both safer and more practical. The following domains use biometric solutions to meet their respective needs:
Legal applications:
Justice and law enforcement: Biometric technology and law enforcement have a very long history, and many very important innovations in identity management have emerged from this beneficial relationship. Today, the biometrics applied by the police force is truly multimodal. Fingerprint, face, and voice recognitions play a unique role in improving public safety and keeping track of the people we are looking for.
Government applications:
Border control and airport: A key area of application for biometric technology is at the border. Biometric technology helps to automate the process of border crossing. Reliable and automated passenger screening initiatives and automated SAS help to facilitate international passenger travel experience while improving the efficiency of government agencies and keeping borders safer than ever before.
Healthcare: In the field of healthcare, biometrics introduces an enhanced model. Medical records are among the most valuable personal documents; doctors need to be able to access them quickly, and they need to be accurate. A lack of security and good accounting can make the difference between timely and accurate diagnosis and health fraud.
Commercial applications:
Security: As connectivity continues to spread around the world, it is clear that old security methods are simply not strong enough to protect what is most important. Fortunately, biometric technology is more accessible than ever, ready to provide added security and convenience for everything that needs to be protected, from a car door to the phone’s PIN.
Finance: Among the most popular applications of biometric technology, financial identification, verification, and authentication in commerce help make banking, purchasing, and account management safer and more convenient and responsible. In the financial area, biometric solutions help to ensure that a customer is the person he/she claims to be when accessing sensitive financial data by entering his/her unique biometric characteristics and comparing them to a model stored in a device or on a secure server. Banking solutions and the payment technologies available today use a wide range of biometric modalities: fingerprints, iris, voice, face, fingerprint, palm veins, behavior, and other types of biometric recognition are all used alone or combined in a multifactorial manner as a system, to lock accounts and serve against fraud.
Mobile: Mobile biometric solutions live at the intersection connectivity and identity. They integrate one or more biometric terms for authentication or identification purposes and take advantage of smartphones, tablets, other types of handhelds, wearable technology, and the Internet of things for versatile deployment capabilities. Thanks to the versatility brought by modern mobile technology, as well as the proliferation of mobile paradigms in the consumer, public, and private world, mobile biometrics is becoming more and more important.
Eye movements tracking applications:
Automotive industry: there is an established relationship between eye movement and attention. Thus, tracking the car driver’s eye movements can be very helpful in measuring the degree of sleepiness, tiredness, or drowsiness. The sleepiness of the driver can be detected by analyzing either blink duration and amplitude or the level of gaze activity [3].
Screen navigation: one of the most important applications for people with disabilities is screen navigation. Using cameras, the application can track a person’s eye movements in order to scroll a web page, write text, or perform actions by clicking on buttons on a computer or mobile devices. Therefore, this kind of application is gaining more attention recently due the rapid development and the growing need of new means of screen navigation especially on mobile devices platforms.
Aviation: the flight simulators track the pilot eye and head movement in order to analyze the pilot’s behavior under realistic circumstances. This simulator is capable of evaluating a pilot’s performance based on his eye movements combined with other information. It can be also used as an important training tool for new pilots in order to help them to look at the primary flight display (PFD) more regularly in order to monitor different airplane indicators.
Detection of dynamic forms is a very important research area that is rapidly evolving in the field of image processing. The goal is to recognize the shapes of objects in an image or in a sequence of images from the information relating to their shapes. In fact, shape is one of the most differentiating features in an image. However, the description and representation of an image remain a major challenge to perform the recognition task.
\nThe quality of a descriptor is represented by its intelligence and ability to distinguish the different forms in a reliable manner despite the geometric variations related to translation and rotation.
\nOn the other hand, a reliable descriptor must withstand the various changes that affect the shape of an object such as noise and distortion that can actually alter the shape and make the recognition task more complicated.
\nThe form representation and description techniques can be generally split into two main classes of methods: contour-based methods and region-based methods. This ranking depends on how the shape features are extracted: from only the outline or the entire region of the shape. For each category, the different approaches are divided into global approaches and local (structural) approaches. This subclassification is based on the representation of the form that depends on the whole form or parts of the form (primitives). These approaches can also be distinguished according to the spatial or transform processing space, in which the shape characteristics are calculated. Global methods are not always robust against occlusions and image noise. In addition, they require an entire and correct segmentation of objects in the images. In general, the segmentation process results in partitioning objects into regions or contour parts that do not necessarily correspond to whole objects.
\nThe contour-based approaches only exploit the boundary of the object for the characterization of the form by ignoring its inner content. The most commonly used representation in contour-based recognition methods is the signature of the form [4]. For a given form, the signature is essentially a representation based on the parameters 1D of the contour of shape. This can be done using a scalar value of the radial distance, angle, curvature, or velocity function. Let us note here that the signature of an entire form (closed curve) is often a periodic function; this will not be the case of a part of form (open curve) for which the two ends are not contiguous. Outline-based descriptors include Fourier descriptors [5, 6], the wavelet descriptors [7, 8], the multi-scale curvature [9], the shape context [10], the contour moments [11], and the symbol chain [12, 13]. Since these descriptors are calculated using only the pixels of the contour, the computational complexity is low, and their characteristic vectors are generally compact.
\nIn region-based approaches, all pixels of the object are considered for characterization of the shape. This type of methods aims to exploit not only the information of the shape boundary but also that of the inner region of the form. The majority of region-based methods use moment descriptors to describe shapes such as Zernike moments [14], Legendre moments [15], or invariant geometric moments [16]. Other methods include grid descriptors [17] or shape matrix [18]. Since the region-based descriptor makes use of all the pixels constituting the shape, it can effectively describe various forms in a single descriptor. However, the size of the region-based features is usually large. This descriptor leads to a computing time that remains considerable.
\nIt remains to emphasize that the description of the forms based on the contour is considered more relevant than that based on the region because the shape of an object is essentially distinguished by the border. In most cases, the central part of the object does not contribute much to pattern recognition [13].
\nIn this chapter, we presented different biometric techniques used in the industrial world as well as their performances.
\nWe started with an overview of biometric systems as well as an overview of biometrics. Then we presented the different issues and challenges related to implementation of such systems.
\nAfter that, we presented a performance evaluation of different biometric systems given the issues and challenges previously stated. Then we presented an overview of some important biometric elements such as the databases and the degree of confidence. Furthermore, a detailed analysis of different domains of application of several biometric techniques was presented with a focus on eye movement tracking techniques.
\nFinally, the different approaches of recognition of dynamic and planar shapes were discussed in the last paragraph.
\nWe have no conflicts of interest to disclose.
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