Biometric Recognition Using Deep Learning: A Survey <s> Fingerprint Recognition <s> Fingerprint verification is one of the most reliable personal identification methods. However, manual fingerprint verification is incapable of meeting today's increasing performance requirements. An automatic fingerprint identification system (AFIS) is needed. This paper describes the design and implementation of an online fingerprint verification system which operates in two stages: minutia extraction and minutia matching. An improved version of the minutia extraction algorithm proposed by Ratha et al. (1995), which is much faster and more reliable, is implemented for extracting features from an input fingerprint image captured with an online inkless scanner. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between minutiae in the input image and the stored template without resorting to exhaustive search and has the ability of adaptively compensating for the nonlinear deformations and inexact pose transformations between fingerprints. The system has been tested on two sets of fingerprint images captured with inkless scanners. The verification accuracy is found to be acceptable. Typically, a complete fingerprint verification procedure takes, on an average, about eight seconds on a SPARC 20 workstation. These experimental results show that our system meets the response time requirements of online verification with high accuracy. <s> BIB001 </s> Biometric Recognition Using Deep Learning: A Survey <s> Fingerprint Recognition <s> A fingerprint recognition approach based on features extracted from the wavelet transform of the discrete image is presented. The efficiency of the method is proven by the high recognition rates achieved using the k-nearest neighbour (k-NN) classifier. The method requires lower computational effort than most of the fingerprint recognition methods proposed to date. <s> BIB002
Fingerprint is arguably the most commonly used physiological biometric feature. It consists of ridges and valleys, which form unique patterns. Minutiae are major local portions of the fingerprint which can be used to determine the uniqueness of the fingerprint BIB001 . Important features exist in a fingerprint include ridge endings, bifurcations, islands, bridges, crossovers, and dots.  . A fingerprint needs to be captured by a special device in its close proximity. This makes making a dataset of fingerprints more time-consuming than some other biometrics, such as faces and ears. Nevertheless, there are quite a few remarkable fingerprint datasets that are being used around the world. Fingerprint recognition has always been a very active area with wide applications in industry, such as smartphone authentication, border security, and forensic science. As one of the classical works, Lee et al [119] used Gabor filtering on partitioned fingerprint images to extract features, followed by a k-NN classifier for the recognition, achieving 97.2% recognition rate. In addition, using the magnitude of the filter output with eight orientations added a degree of shift-invariance to the recognition scheme. Tico et al BIB002 extracted wavelet features from the fingerprint to use in a k-NN classifier.