Region of Interest Localization Methods for Publicly Available Palmprint Databases

2.1.1 The Hong Kong Polytechnic University (PolyU) Palmprint Database

The PolyU Palmprint Database is the first publicly available palmprint database. It contains 7752 images captured from 386 hands in two sessions; around 10 images are collected for each palm in each session. The palmprint acquisition device is a contact-based device that consists of a high-quality industrial monochrome camera and a well-selected ring light source. The palm pose also is restricted by the pegs. So the captured images have high image qualities. However, the released image resolution, 384×284pixels, is relatively low.

2.1.2 PolyU Palmprint Multi-spectral Database

Authentication by just RGB or gray images may not be safe enough; attacks from fake palm images and videos can easily spoof the system. Hence, the multi-spectral based palmprint recognition starts to draw attention. Four spectrums, red, green, blue, and near-infrared (NIR), are utilized to establish the PolyU Multi-spectral Palmprint Database. A contact-based device is employed to capture images from 250 volunteers in two sessions. 24,000 images are captured from 500 palms. Each palm contributes six images in each session under each spectrum. Our observation shows that the images captured under blue light have the highest sharpness, while that captured under NIR light has the lowest.

2.1.3 CASIA Palmprint Image Database

In CASIA Palmprint Database, 5502 images are captured from 312 subjects, around 9 images for each palm. The authors also made their own image acquisition device, which has a big enclosure and a black backboard. During the capture process, the user puts his/her palm into the enclosure back on the unicolor board; the ambient light is blocked by the enclosure. Hence, the palm images are captured in an ideal environment, but the sharpness of the palm images is not very high. Besides, some palm images are captured with significant rotations, and some fingers have moved out of the imaging window. These factors make it difficult to localize the palmprint ROIs.

2.1.4 CASIA Multi-spectral Palmprint Image Database V1.0

CASIA-MS-Palmprint V1 is a touchless multi-spectral palmprint database collected under six spectrums in two sessions. The 460, 630, 700, 850, 940 nm and WHITE spectrums are employed in their self-developed device. However, the image sharpness also is not very high compared with the PolyU multi-spectral database in which the palm is captured by a contact-based device. This database contains 7200 palm images captured from 200 different palms. For each session, under each light spectrum, three images are captured from each palm.

2.1.5 IIT Delhi (IITD) Touchless Palmprint Database version 1.0

The palm images in the IITD palmprint database are captured with large rotation variations. The touchless imaging setup consists of a big black box, a digital camera, and a circular fluorescent light source. It provides 2601 palm images collected from 470 hands, including 1301 left-palm images and 1300 right-palm images. The official ROI images have been normalized and thus show obvious principal lines and wrinkles.

2.1.6 PolyU-IITD Contactless Palmprint Images Database version 3.0

This database is collected from the volunteers in different countries, China and India, by a general-purpose handheld camera over the years. Totally 14,000 images are captured from 1400 palms. The characteristic of this database is that the images are collected across different locations, times, occupations, and age ranges. Both normal and abnormal hands are involved (as is shown in Figure 1(k)).

2.1.7 COEP Palmprint Database

The palm images in this database have high resolutions. They are captured by Canon PowerShot SX120 IS, the image resolution is 1600×1200pixels. According to the file’s attribute information, the image reaches 180 dots per inch (DPI). Most of the ridge and valley lines could be seen clearly. During the image capture process, the palm position is restricted by five pegs, so the captured images are with low rotation degrees. It is a good dataset for studying the palmprint image sharpness. The downloaded dataset contains 1305 images pertaining to 167 palms, around 8 images per palm.

2.1.8 GPDS100Contactlesshands2Band Database

Both visible light camera and infrared (IR) light camera are adopted to collect 1000 visible light palm images and 1000 IR light palm images from 100 volunteers. Each palm contributes 10 visible light images and 10 IR light images. The user places his/her palm over the camera and touchlessly adjusts the position and pose of the hand in order to overlap with the hand mask drawn on the device screen. The image sharpness is not very high. However, it is a meaningful database because the images’ qualities are more close to that captured in real-world applications.

2.1.9 KTU CVPR Lab. Contactless Palmprint Database

The author made a new device by a low-cost camera to capture palm images with a resolution of 768×576at 75 DPI. Totally 1752 images are collected from 145 palms, about 12 images for each palm. The images are captured under different ambient light intensities, backgrounds, finger postures (finger space and finger rings), and different hand distances, rotations, and translations. The image sharpness level of this database is not very high.

2.1.10 Tongji Palmprint Database

It was the biggest touchless palmprint database in 2017. The authors also made a novel palmprint acquisition equipment that consists of a digital camera, a ring light source, a screen, and a vertical enclosure. This device can capture both visible light palmprint images and infrared light palm vein images. During collection, the user’s palm is put into the enclosure to avoid ambient light. At the same time, the upper screen will show the palm in real time, so that the user knows how to put his/her hand and when to stop and hold. Totally 12,000 images are captured from 600 hands in 2 sessions. For each palm, in each session, 10 palmprint images are collected.

2.1.11 Tongji Mobile Palmprint Dataset

The device used in Tongji Palmprint Database provides a stable environment for palmprint acquisition; this strategy can ensure the final recognition performance. However, the big enclosure also has limited its applications. So the Tongji group further collected another novel database by the widely used mobile phones. The palm images are captured in the natural indoor environment. Two mobile phones are used, including HUAWEI and Xiaomi. This dataset contains 16,000 palmprint images from 400 palms collected in 2 sessions. In each session, each mobile phone captures 10 images for each palm. All palm images are labeled, and corresponding codes are released on the author’s homepage [23].

2.1.12 Xi’an Jiao Tong University (XJTU) Unconstrained Palmprint Database

The XTJU-UP databases [26] are collected by five mobile phones, including iPhone 6S, HUAWEI Mate 8, LG G4, Samsung Galaxy Note5, and Xiaomi MI8, with and without the built-in flashlight source. There are 100 volunteers; each palm provides 10 images for each phone, under each illumination condition. So, totally 20,000 images are captured (100×2×10×5×2).

2.1.13 Nanyang Technological University (NTU) Palmprint databases version 1

The NTU palmprints from the Internet (NTU-PI-v1) database consists of 7781 hand images collected from the Internet. Hence, the palm images are captured in an uncontrolled and uncooperative environment. The images in it are collected from 2035 different palms of 1093 subjects with different ethnicity, sex, and age. Around four images are collected for each palm. It is the first large database established for studying palmprint recognition in the wild. But the image sharpness is relatively low compared with the normal palmprint images. The NTU Contactless Palmprint Database (NTU-CP-v1) contains 2478 palm images captured from 655 palms of 328 subjects using cameras of Canon EOS 500D or NIKON D70s, around four images for each palm. Currently, the samples for each category are relatively few compared with the other databases.

2.2.1 Classical methods

One important goal of the first strategy mentioned above is simplifying the background. There exist three approaches:

1. Capture the palm with a unicolor backboard [4, 6, 8, 12, 21, 33]

2. Employ an IR camera or a depth camera to capture an IR image or a depth image to assist palm segmentation [34, 10, 30, 35]

3. Enhance the contrast of the foreground and background by setting a strong light source intensity and a short exposure time

Their target is enhancing the contrast of the palm region and the background. For example, in [28], the mobile phone’s built-in LED flash is utilized for palm segmentation. When the flash is turned on, the palm surface is much brighter than the background, because the palm is much closer to the camera than the background. The built-in auto-exposure control function of the image signal processor (ISP) on the camera chip will automatically decrease the exposure time to capture proper palm images; the palm region should fall into the proper grayscale range. As a result, the captured background is very dark.

After hardware and acquisition mode optimization, the palm region could be segmented by skin-color thresholding or the Otsu-based methods [30, 31, 36, 37]. Maximum-connected-region detection is useful to delete the background noise. After palm region image is obtained, there are four approaches to detect the valley points:

1. Competitive valley detection algorithm [35], which traverses each contour pixel by testing and comparing its neighbor pixels’ grayscale values. After palm segmentation, a binary palm image is obtained. The pixel on the palm contour is tested, taking the current contour pixel as the center point, and then 4, 8, and 16 testing points are placed around it, respectively. If in all the three tests, the pixels’ values meet the predefined conditions, a line will be drawn from the center point toward the non-hand region. If this line does not cross any hand region, this center pixel is considered as a valley location. In the same way, we can find the other candidate valley points.

2. Line-scan-based methods [4, 27, 33]. After rotation normalization, the pixels are tested through a row or a column according to the specific hand orientation. In the segmented hand image, the hand region pixels are set as white, and the background pixels are set as zero. So once the pixel value changes from white to black or from black to white, the keypoints of the finger contour are detected. Then, the finger valleys can be obtained by edge tracking (as is shown in Figure 2(c)).

3. Local-extremum-based methods [30, 33, 38, 39, 40, 41]. As is shown in Figure 2(a), by selecting a point as the start point, we can calculate the distances between the start point and all the palm contour points to generate a distance curve. Then, on the distance curve, the local maximum points correspond to fingertips; the local minimum points correspond to finger valley points. The finger valleys could be segmented from the palm contour around the detected valley points. Then the tangent line of the two finger valleys can be detected as the reference line.

4. Convex hull-based methods [28, 42, 43]. The minimum polygon is detected to encapsulate the palm contour. Generally, the fingertips are vertexes of the convex hull. Then, the finger valleys and the valley points could be obtained as the methods mentioned above.

Generally, after the four finger valley points are obtained, we should get to know whether this hand is left or right so that the two desired valley points can be determined. As to how to identify the left and right hand, literature [35] uses geometric rules of the coordinates; literature [30] utilizes the valley areas, generally, the valley area between thumbs and index finger is bigger than that between the little and ring finger; literature [41] trained a CNN to classify it; the method proposed in [44] does not need to know the left or right information.

Rotation normalization and scale normalization are two key problems lie in palmprint preprocessing. In [33], the authors analyzed the existing methods and provided their optimized solutions in palm width detection and center point generation. Rotation normalization aims to rotate all the palms to a standard direction. To determine the main direction of the palm, many methods have been proposed. In [40], principal component analysis (PCA) is utilized to estimate the rotation angle of the palm. In [17, 44], the author utilized the training set to learn a regression model which can map the landmarks’ coordinates to the palm’s main direction. Hence, after landmark detection, the palm direction can be obtained by the regression model. In [30], the line crossing the middle fingertip and the palm center point is treated as the palm’s center line; the palm’s orientation is estimated by the line’s slope.

The center point of the palm could be determined by different methods, such as the centroid of the palm region [40], the center point of the palm’s maximum inscribed circle [30], the point which reaches the maximum distance value after distance transform [45, 46], or the shift from the middle point of the palm width line detected based on heart line [33].

With the information of the hand rotation angle, the palm image could be normalized to the standard direction. Then, what we need to do is scale normalization, which means to determine the side length of the ROI. The work reported in [10, 17, 21, 30, 33, 47] utilized the palm width to determine the size of the ROI, while the work reported in [4, 27, 28, 29] utilized the length of the tangent line to determine the size of the ROI. In [30], the author found that big ROI performs better. Perhaps big regions can decrease the influences of the misalignment. Here, we provide two examples for better understanding the whole process of ROI extraction.

In [27], the center block (13×13pixels) of the image is utilized to train the skin-color model, and then the palm region is segmented based on skin-color thresholding. The candidate landmarks are obtained using the method proposed in [35]. The author proposed a two-stage strategy to achieve high robustness, i.e., palms with very big rotations or imperfect hand segmentations. In the first stage, the coarse palm direction is detected. Each candidate valley point will generate its own direction angle, and the angles will be partitioned into four coarse directions, namely, up, down, left, and right. The coarse main direction is the one which has the most supporters, and the inconsistent angles will be deleted. Then, the palm direction is calculated from the remaining angles. In the second stage, the palm image is rotated so that the four fingers point to the standard direction, and then the line-scan-based method is used to track the finger valleys. Similarly, after the valley points are detected, the ROI is derived in accordance with the reference line generated by the two valley points.

In [30], the palm is segmented from the IR light palm image by the Otsu and maximum connected domain algorithms. Then the center point of the palm is determined by the maximum inscribed circle. Right of the center point, a start point could be set. Then, the two-phase keypoint detection method is utilized for detecting the finger tips and valleys. First, the distance curve is generated by the start point and the palm contour points, and the fingertip of the middle finger is then obtained. Based on the fingertip and the center point, a new reference point could be generated to replace the start point used in the first phase. Then, with the palm orientation information, a new distance curve is generated. The precise fingertips and valley points are finally detected by the extremum points of this new distance curve. The tangent line of the valleys around the two detected valley points are obtained (as is shown in Figure 2(a)); we scan the palm region using lines which are parallel with the tangent line. Each line provides a palm width value, and the final palm width is determined by their median value. Last, the ROI is derived according to the reference line and the palm width.

2.2.2 New-generation methods

The methods mentioned above are all based on traditional digital image processing techniques. Most of them just utilized the edge information of the palm. However, it is not sufficient and it leads the algorithms being sensitive to palm postures and background objects. In recent years, many new methods have been proposed, such as the active shape model (ASM)-based methods [48, 49], the active appearance model (AAM)-based methods [17, 29, 50], the regression tree-based methods [47], and the deep learning-based methods [24, 41]. The new-generation methods utilized both the edge and texture information to learn much more robust models to regress the landmarks. The main stages of palmprint ROI localization is detecting the palm region from the whole image, regressing the landmarks, determining the palm orientation and width, establishing local coordinate system, and computing the ROI locations.

In [17, 44], 25 hand landmarks are selected to form a shape, including 10 end points and 15 landmarks of the finger valleys and palm boundary. This shape convers the finger roots and the interdigital regions of the palm. By AAM algorithm, both the hand shape and the palm texture information are utilized, the shape and corresponding landmark points can automatically reshape itself to fit the real hand contour. To evaluate the localization performance, the authors proposed a modified point-to-curve distance and a margin width metric. Since the initial position of the shape model is critical to the regression performance, the fitting process is divided into two stages. At first five rotations and five scale factors are used to generate 25 initial shapes. After regression, only the shape models, which obtain the 15 optimal reconstruction errors, are passed to the second stage for fine-grained regression.

In [41], the authors proposed a CNN framework based on LeNet [51] to detect the finger valley points. The proposed network involves convolutional layers and fully connected layers; the output is a six-dimensional vector corresponding to the three valley points between fingers excluding the thumb. In their work, two neural networks are designed: one is for identifying whether the hand is left or right, and another is for landmark localization. According to their experiments, the first network can perfectly identify the hand being a left or right hand, and the landmark localization performance is better than the classical method which is based on Otsu segmentation and Zhang’s ROI localization algorithm [4].

In [24], based on VGG-16 [52], the authors designed an end-to-end neural network to localize the hand landmarks, generate the aligned ROI, and do feature extraction and recognition tasks at the same time. The hand region is extracted from the original Internet image, and then it is resized to 227×227pixels. The normalized palm image will be put into the designed CNN for aligned ROI localization, feature extraction, and classification. The proposed network consists of two subnetworks, ROI Localization and alignment network (ROI-LAnet) and feature extraction and recognition network (FERnet). More than three landmarks are determined by the author in order to be able to parametrize non-rigid transformations.

In [47], at first the palm position is detected by techniques of sliding window, histogram of oriented gradient (HOG) [53], and support vector machine (SVM). In the training set, 14 landmark points are determined and labeled manually. After landmark point regression, the reference line is established by the two valley points. The position of the center point and the side length of the ROI both are determined by the palm width.

However, there still exist some challenging problems in ROI localization waiting for better solutions:

1. Palm region segmentation under complex backgrounds

2. Keypoint detection on palms with closed or incomplete fingers

3. Keypoint detection on palms with big rotations

4. Left and right hand detection on palms having long thumbs

5. Palm scale (palm width) determination under various palm and finger poses in touchless scenarios

Compared with the new-generation methods which need to label the landmarks manually and train the regression model, the classical methods based on hardware and capture mode optimization and digital image processing algorithms are easier to use. They also can achieve high localization precisions due to its strict imaging conditions. Hence, in this chapter we still utilize the classical methods to extract the ROIs for different palmprint databases.