Biometric Recognition Using Deep Learning: A Survey <s> Palmprint Recognition <s> As the important implementation of biometric technology, palmprint verification is one of the most reliable personal identification methods. In this paper, a new approach to palmprint verification based on line feature matching and datum point invariance is presented. The datum points of palmprint which act as the important registrations, are defined owing to their remarkable advantage of invariable location. Then, the simple and efficient methods of line feature extraction and matching are proposed. Several palmprint images are adopted to test the approach and the experimental results show the effectiveness of palmprint verification. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Palmprint Recognition <s> Abstract As the first attempt of automatic personal identification by palmprint, in this paper, two novel characteristics, datum point invariance and line feature matching, are presented in palmprint verification. The datum points of palmprint, which have the remarkable advantage of invariable location, are defined and their determination using the directional projection algorithm is developed. Then, line feature extraction and line matching are proposed to detect whether a couple of palmprints are from the same palm. Various palmprint images have been tested to illustrate the effectiveness of the palmprint verification with both characteristics. <s> BIB002 </s> Biometric Recognition Using Deep Learning: A Survey <s> Palmprint Recognition <s> The palmprint has one special salient feature which is not salient in fingerprint. That is the crease. In a palmprint the creases are large in number and comparatively easy to extract. Creases are also approximately stable in a person's whole life, which qualifies them as features in palmprint recognition. We give creases an accurate definition which is fit for algorithm implementation. We devised a rather exquisite algorithm to extract all the creases in a palmprint whose success is mainly from a new different direction computing method and a thorough local analysis and a robust search algorithm. Based on the extracted creases, we devised a robust palmprint matching algorithm which is rotation and translation invariant. The crease extraction results and palmprint matching results show that the crease can be extracted successfully and crease-based palmprint matching is robust and accurate. <s> BIB003 </s> Biometric Recognition Using Deep Learning: A Survey <s> Palmprint Recognition <s> Palm print is a new biometric method to recognize a person. Features in a palm print include principal lines, wrinkles and ridges, etc. The fact that the principal lines, wrinkles and ridges have different resolutions motivates us to analyze the palm print using a multiresolution analysis method. A novel palm print feature, named wavelet energy features (WEF), is defined employing wavelet which is a powerful tool of multiresolution analysis. In this paper. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in several directions at different wavelet decomposition levels (scales), so its ability to discriminate palms is very strong. To compute with ease is another virtue of WEF. The very high recognition rates obtained in experiments shows the effectiveness of the proposed method. <s> BIB004 </s> Biometric Recognition Using Deep Learning: A Survey <s> Palmprint Recognition <s> Palmprint is one of the relatively new physiological biometrics due to its stable and unique characteristics. The rich texture information of palmprint offers one of the powerful means in personal recognition. According to psycho-physiology study, the primary visual cortex in the visual area of human brain is responsible for creating the basis of a three-dimensional map of visual space, and extracting features about the form and orientation of objects. The basic model can be expressed as a linear superposition of basis functions. This idea inspired us to implement two well known linear projection techniques, namely Principle Component Analysis (PCA) and Independent Component Analysis (ICA) to extract the palmprint texture features. Two different frameworks of ICA [1] are adopted to compare with PCA for the recognition performances by using three different classification techniques. Framework I observed images as random variables and the pixels as outcomes while framework II treated pixels as random variables and the images as outcome. We are able to show that ICA framework II yields the best performance for identifying palmprints and it is able to provide both False Acceptance Rate (FAR) and False Rejection Rate (FRR) as low as 1%. <s> BIB005 </s> Biometric Recognition Using Deep Learning: A Survey <s> Palmprint Recognition <s> Palmprint images contain rich unique features for reliable human identification, which makes it a very competitive topic in biometric research. A great many different low resolution palmprint recognition algorithms have been developed, which can be roughly grouped into three categories: holistic-based, feature-based, and hybrid methods. The purpose of this article is to provide an updated survey of palmprint recognition methods, and present a comparative study to evaluate the performance of the state-of-the-art palmprint recognition methods. Using the Hong Kong Polytechnic University (HKPU) palmprint database (version 2), we compare the recognition performance of a number of holistic-based (Fisherpalms and DCT+LDA) and local feature-based (competitive code, ordinal code, robust line orientation code, derivative of Gaussian code, and wide line detector) methods, and then investigate the error correlation and score-level fusion performance of different algorithms. After discussing the achievements and limitations of current palmprint recognition algorithms, we conclude with providing several potential research directions for the future. <s> BIB006 </s> Biometric Recognition Using Deep Learning: A Survey <s> Palmprint Recognition <s> Palmprint recognition has drawn a lot of attentions during recent years. Different features and algorithms have been proposed for palmprint recognition in the past such as Gabor-based features, wavelet features, and histogram of oriented lines. In this paper, a powerful image representation, so called deep scattering network, is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. Scattering transform is designed such that the features in its first layer are similar to SIFT descriptors and the higher layers' features capture higher frequency content of the signal which are lost in SIFT. After extraction of scattering features, their dimensionality is reduced by applying principal component analysis (PCA). By doing so, a great amount of computation complexity can be reduced. At the end, the recognition is performed using two different classifiers, multi-class SVM and minimum-distance classifier. The proposed scheme has been tested on a well-known palmprint database and achieved accuracy rates of 99.4% and 99.9% using minimum distance classifier and SVM respectively, outperforming previous algorithms on this dataset. <s> BIB007
Palmprint is another biometric which is gaining more attention recently. In addition to minutiae features, palmprints also consist of geometry-based features, delta points, principal lines, and wrinkles BIB002 BIB006 . Each part of a palmprint has different features, including texture, ridges, lines and creases. An advantage of palmprints is that the creases in palmprint virtually do not change over time and are easy to extract BIB003 . However, sampling palmprints requires special devices, making their collection not as easy as other biometrics such as fingerprint, iris and face. Classical works on palmprint recognition have explored a wide range of hand-carfted features such as as PCA and ICA BIB005 , Fourier transform  , wavelet transform BIB004 , line feature matching BIB001 , and deep scaterring features BIB007 .