\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"}]},book:{item:{type:"book",id:"8149",leadTitle:null,fullTitle:"Selected Aspects of Non-Profit Organisations",title:"Selected Aspects of Non-Profit Organisations",subtitle:null,reviewType:"peer-reviewed",abstract:"The volatility of the economy, political instability, and greater demands on not-for-profit organisations (non-profits) will require considerable rethinking and refocusing for many organisations. 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How long\nit takes you to fully recover from your surgery will depend on many things, including:Nowadays, face recognition is a non-intrusive biometric method in which the data acquisition is easy and can be carried out with or without the cooperation of the person under analysis. The face can be considered as the easiest way to recognize a person, increasing the acceptance of this kind of systems and their applications [1, 2, 3]. These systems consist of two tasks: identity verification, where the system verifies if the identity of the person is that which he/she claims it to be, and identification task, where the system determines the identity of the person among all the people in a database. Thus, the recognition task covers both tasks–identification and verification [4, 5].
\nSeveral problems must be considered in the development of a face recognition system such as illumination changes, facial expressions, and partial occlusions. It is because these kinds of changes can harm the accuracy of a face recognition system [6]. The changes in lighting conditions have received significant attention [6]. Because of that, a lot of systems were proposed in the last years, trying to reduce these problems [6]. Some systems proposed to this end are based on image processing techniques such as histogram equalization [6, 7] and contrast-limited adaptive histogram equalization (CLAHE) [8]. Another way to solve the problem of illumination changes is the development of different high-performance methods to solve these kinds of changes such as the eigenphases approach [7, 8, 9, 10, 11]. Also, are useful some methods based on frequency transforms like discrete cosine transform [12, 13, 14], discrete Gabor transform [15, 16, 17], discrete wavelet transforms [18, 19, 20, 21], and discrete Haar transform [22]. Additional methods that could be applied are the eigenfaces [23, 24] which use the principal component analysis (PCA) [25, 26], modular PCA-based face recognition methods [27], the Fisherfaces approach [28], and the Laplacianfaces [29].
\nThe local binary pattern (LBP) operator [30] has recently been proposed in several applications. The principal advantages of this algorithm are that it has a good computational performance and presents a good support when the images have gray-level changes. Because of that LBP can be applied for image characterization in several pattern recognition tasks [31]. This algorithm can be used for face characterization because the face images have a lot of little patterns which can be characterized using the LBP [30]. Several LBP variations have been proposed such as: the holistic LBP histogram (hLBPH) [30], the spatially enhanced LBP histogram (eLBPH) [32], holistic LBP Image algorithm (hLBPI) [32] and decimated image window binary pattern (WBP) [33]. All of these algorithms are based on the original LBP algorithm, but the computational complexity of the hLBPI and WBP are lower than the others providing also a good performance as shown in this chapter.
\nIn recent years, the interest in the face recognition schemes has increased because of its potential implementation in mobile devices, which generally have a limited computational power. Hence, this chapter presents a comparison of the texture descriptors like hLBPI and WBP. Finally, some classification methods, like SVM, Euclidean distance, and cosine distance, are used to perform the recognition. In this chapter, these algorithms were evaluated with different illumination and facial expression changes.
\nThe remainder of this chapter is organized as follows: Section 2 presents the description of the evaluated system. Section 3 presents the evaluation results. Finally, Section 4 provides the conclusions of this research.
\nFigure 1 shows the block diagram of the evaluated face recognition system, where, firstly, the system receives the face image under analysis. It is then fed into an interpolation stage (Any other method can be applied.). Next, the texture descriptor algorithm is applied to characterize the image. Finally, the feature matrix is fed into the classification stage.
\n(a) Block diagram of the evaluated face recognition scheme, (b) illustration of the evaluated face recognition scheme.
In this chapter were used two texture descriptors, the hLBPI and the WBP. The hLBPI algorithm, introduced by Ojala et al. [34], is based on the original LBP method. This algorithm uses masks of \n
Example of implementation of LBP in a neighborhood of 3 × 3 pixels.
After obtaining the LBP matrix, can estimate an L-dimensional feature vector, where L is the total number of training images of \n
where \n
A WBP example is shown in Figure 3. First, the original image is divided into windows of \n
WBP example.
A comparison of the computational complexity of other recently proposed methods for feature vector estimation during the testing operation is shown in Figure 4.
\nComputational complexity of different algorithms.
After obtaining the feature vectors, the next step is the classification stage, which involves performing the identification or verification task. In this chapter, in the training stage, the K-means was used to obtain a template by averaging the training images of each class. During the identification task, three classifiers are used. These are SVM, the Euclidean distance, and cosine distance, classifying the image under analysis as belonging to the N-class with the smaller distance or the largest probability in the SVM case. During the verification task, the system validates the identity of the person under analysis with a given threshold, and these results are only obtained by the SVM. In this chapter, two different distances were evaluated, the Euclidean distance given by:
\nand the cosine distance is given by.
\nwhere \n
In this chapter, the system was evaluated using the identification and verification tasks, wherein identification of the system is required to determine the identity of the person under analysis by comparing the facial characteristics stored in a database with the face characteristics extracted and the verification task, where the system must define if the identity corresponds with the person she/he claims to be [4]. In both tests, the results are compared with another algorithm like the eigenphase [11], Laplacianfaces [29], Fisherfaces, and eigenfaces [28], all of them used in the classification task with the SVM and k-means with Euclidean and cosine distance, the AR Face Database [35] was used for all tests.
\nThe AR database was expanded with four additional images for each one in the original AR database. These images are shown in Figure 5, where Figure 5a is the original image and Figure 5b–e show the resulting images of the illumination variations.
\nEffects of illumination transformation applied to form the extended AR database.
After the expansion, the AR database has 12,000 face images in total, each person has 100 images and the database has 120 persons, where 65 are males and 55 females. The database was divided into two sets, the AR(A) which has 70 images with illumination and expression changes and the AR(B) which has 30 images with partial occlusion using sunglasses and also illumination changes. Figure 6 shows some examples of these images.
\nExamples of (a) images from the AR(A) set. (b) Images from the AR(B) set.
In real-world applications, the number of training images and recognition accuracy are strongly related; as more images are used for training, the recognition accuracy improves. However, in real applications the number of training images is limited. Figure 7 shows the performance of hLBPI (Figure 7a) and WBP (Figure 7b) with different number of training images using three different classifiers.
\nThe identification rate obtained using different numbers of training images.
Figure 8 shows the recognition performance of the texture descriptors hLBPI and WBP compared with the other classical methods, all of them using the set AR(A) and seven training images for each person.
\nRecognition performance of the evaluating approach using: SVM, Euclidean distance, and cosine distance in the classification stage. The performance of hLBPI, eigenphases, eigenfaces, Laplacianfaces, and Fisherfaces are also shown for comparison.
An important evaluation to obtain also is the ranking of identification, where the ranking (n) denotes the probability that an image belongs to one of n classes with highest probability. That is, a ranking of 10 is the probability that the image belongs to one of the 10 most likely persons. Figures 9 and 10 present the ranking evaluation with the set AR(A) and set AR(B).
\nRanking evaluation of image set AR(A).
Ranking evaluation of image set AR(B).
In all cases, the training was done using seven images per person belonging to either the AR(A), while the recognition system was tested with images that were not used for training from the AR(A) and AR(B) sets respectively.
\nIn the case of verification, the percentage of error is divided into two: false acceptance and false rejection. False acceptance occurs when an individual claims to be the person who is not and this is mistakenly accepted by the system. False rejection occurs when an individual provides their identity and the system erroneously rejects this statement.
\nFigure 11 shows the receiver operating characteristics (ROC) when the hLBPI and WBP algorithms are used for the verification task using the set AR(A). Figure 11 shows that both algorithms have a similar performance with a false rejection and false acceptance, and they are very low.
\nROC of proposed and conventional hLBPI algorithms.
The performance of most face verification systems depends on a suitable selection of the threshold value to diminish both the false acceptance and the false rejection. After many tests, it was concluded that this relationship, this threshold, is used to decide if the person is who she/he claims to be; next, it is explained, how the threshold can be obtained with an exponential function as shown in Figure 12, where you can select the point that has the lowest value of both false acceptance and false rejection. Thus, we can assume that the false acceptance probability is given by:
\nRelationship between the false acceptance rate and the threshold value.
and
\nThen from Eqs. (4) and (5) it follows that \n
Figure 13 shows the results of an exponential function and some experimental results of the relationship between the false acceptance rate and false rejection rate in this chapter.
\nRelationship between the false acceptance rate and false rejection rate.
This chapter presented the application of different texture descriptors for tasks ranging from feature extraction to face recognition, which are based on the LBP algorithm, specifically the hLBPI and WBP. The evaluation results demonstrate that these algorithms provide good recognition rates. In most situations, the accuracy of recognition with the WBP is slightly lower than the accuracy with the hLBPI because the feature vector estimation of the WBP does not require PCA and uses non-overlapping blocks. This fact results in an important computational complexity reduction of approximately \n
The evaluation results using the AR databases demonstrate that these algorithms provide good results also when it performs identity verification tasks, providing a theoretical criterion which allows selecting the threshold such that the system be able to provide a previously specified false acceptance or false rejection rate.
\nWe thank the National Science and Technology Council of Mexico and to the Instituto Politecnico Nacional for the financial support during the realization of this chapter.
\nAs an Open Access publisher, IntechOpen is dedicated to maintaining the highest ethical standards and principles in publishing. In addition, IntechOpen promotes the highest standards of integrity and ethical behavior in scientific research and peer-review. To maintain these principles IntechOpen has developed basic guidelines to facilitate the avoidance of Conflicts of Interest.
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\\n\\nA Conflict of Interest is a situation in which a person's professional judgment may be influenced by a range of factors, including financial gain, material interest, or some other personal or professional interest. For IntechOpen as a publisher, it is essential that all possible Conflicts of Interest are avoided. Each contributor, whether an Author, Editor, or Reviewer, who suspects they may have a Conflict of Interest, is obliged to declare that concern in order to make the publisher and the readership aware of any potential influence on the work being undertaken.
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\\n\\nEditors can also have Conflicts of Interest. Editors are expected to maintain the highest standards of conduct, which are outlined in our Best Practice Guidelines (templates for Best Practice Guidelines). Among other obligations, it is essential that Editors make transparent declarations of any possible Conflicts of Interest that they might have.
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\\n\\nCONFLICT OF INTEREST - REVIEWER
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\\n\\nEXAMPLES OF CONFLICTS OF INTEREST:
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\\n\\nAuthors are required to declare all potentially relevant non-financial, financial and material Conflicts of Interest that may have had an influence on their scientific work.
\\n\\nAcademic Editors and Reviewers are required to declare any non-financial, financial and material Conflicts of Interest that could influence their fair and balanced evaluation of manuscripts. If such conflict exists with regards to a submitted manuscript, Academic Editors and Reviewers should exclude themselves from handling it.
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\\n\\nAcademic Editors should declare if the Author of a submitted manuscript is affiliated with the same department, faculty, institute, or company as they are.
\\n\\nPolicy last updated: 2016-06-09
\\n"}]'},components:[{type:"htmlEditorComponent",content:"In each instance of a possible Conflict of Interest, IntechOpen aims to disclose the situation in as transparent a way as possible in order to allow readers to judge whether a particular potential Conflict of Interest has influenced the Work of any individual Author, Editor, or Reviewer. IntechOpen takes all possible Conflicts of Interest into account during the review process and ensures maximum transparency in implementing its policies.
\n\nA Conflict of Interest is a situation in which a person's professional judgment may be influenced by a range of factors, including financial gain, material interest, or some other personal or professional interest. For IntechOpen as a publisher, it is essential that all possible Conflicts of Interest are avoided. Each contributor, whether an Author, Editor, or Reviewer, who suspects they may have a Conflict of Interest, is obliged to declare that concern in order to make the publisher and the readership aware of any potential influence on the work being undertaken.
\n\nA Conflict of Interest can be identified at different phases of the publishing process.
\n\nIntechOpen requires:
\n\nCONFLICT OF INTEREST - AUTHOR
\n\nAll Authors are obliged to declare every existing or potential Conflict of Interest, including financial or personal factors, as well as any relationship which could influence their scientific work. Authors must declare Conflicts of Interest at the time of manuscript submission, although they may exceptionally do so at any point during manuscript review. For jointly prepared manuscripts, the corresponding Author is obliged to declare potential Conflicts of Interest of any other Authors who have contributed to the manuscript.
\n\nCONFLICT OF INTEREST – ACADEMIC EDITOR
\n\nEditors can also have Conflicts of Interest. Editors are expected to maintain the highest standards of conduct, which are outlined in our Best Practice Guidelines (templates for Best Practice Guidelines). Among other obligations, it is essential that Editors make transparent declarations of any possible Conflicts of Interest that they might have.
\n\nAvoidance Measures for Academic Editors of Conflicts of Interest:
\n\nFor manuscripts submitted by the Academic Editor (or a scientific advisor), an appropriate person will be appointed to handle and evaluate the manuscript. The appointed handling Editor's identity will not be disclosed to the Author in order to maintain impartiality and anonymity of the review.
\n\nIf a manuscript is submitted by an Author who is a member of an Academic Editor's family or is personally or professionally related to the Academic Editor in any way, either as a friend, colleague, student or mentor, the work will be handled by a different Academic Editor who is not in any way connected to the Author.
\n\nCONFLICT OF INTEREST - REVIEWER
\n\nAll Reviewers are required to declare possible Conflicts of Interest at the beginning of the evaluation process. If a Reviewer feels he or she might have any material, financial or any other conflict of interest with regards to the manuscript being reviewed, he or she is required to declare such concern and, if necessary, request exclusion from any further involvement in the evaluation process. A Reviewer's potential Conflicts of Interest are declared in the review report and presented to the Academic Editor, who then assesses whether or not the declared potential or actual Conflicts of Interest had, or could be perceived to have had, any significant impact on the review itself.
\n\nEXAMPLES OF CONFLICTS OF INTEREST:
\n\nFINANCIAL AND MATERIAL
\n\nNON-FINANCIAL
\n\nAuthors are required to declare all potentially relevant non-financial, financial and material Conflicts of Interest that may have had an influence on their scientific work.
\n\nAcademic Editors and Reviewers are required to declare any non-financial, financial and material Conflicts of Interest that could influence their fair and balanced evaluation of manuscripts. If such conflict exists with regards to a submitted manuscript, Academic Editors and Reviewers should exclude themselves from handling it.
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\n\nAuthors should declare if they are board members of an organization that could benefit financially or materially from the publication of their work.
\n\nAcademic Editors should declare if they were coauthors or they have worked on the research project with the Author who has submitted a manuscript.
\n\nAcademic Editors should declare if the Author of a submitted manuscript is affiliated with the same department, faculty, institute, or company as they are.
\n\nPolicy last updated: 2016-06-09
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