Learning features for offline handwritten signature verification using deep convolutional neural networks

https://www3.ntu.edu.sg/home/AdamsKong/publication/BioHashing.pdf

An analysis of BioHashing and its variants

Cancellable biometrics and annotations on BioHash

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.

A Comparative Study of Palmprint Recognition Algorithms

To replace compromised biometric templates, cancelable biometrics has recently been introduced. The concept is to transform a biometric signal or feature into a new one for enrollment and matching. For making cancelable fingerprint templates, previous approaches used either the relative position of a minutia to a core point or the absolute position of a minutia in a given fingerprint image. Thus, a query fingerprint is required to be accurately aligned to the enrolled fingerprint in order to obtain identically transformed minutiae. In this paper, we propose a new method for making cancelable fingerprint templates that do not require alignment. For each minutia, a rotation and translation invariant value is computed from the orientation information of neighboring local regions around the minutia. The invariant value is used as the input to two changing functions that output two values for the translational and rotational movements of the original minutia, respectively, in the cancelable template. When a template is compromised, it is replaced by a new one generated by different changing functions. Our approach preserves the original geometric relationships (translation and rotation) between the enrolled and query templates after they are transformed. Therefore, the transformed templates can be used to verify a person without requiring alignment of the input fingerprint images. In our experiments, we evaluated the proposed method in terms of two criteria: performance and changeability. When evaluating the performance, we examined how verification accuracy varied as the transformed templates were used for matching. When evaluating the changeability, we measured the dissimilarities between the original and transformed templates, and between two differently transformed templates, which were obtained from the same original fingerprint. The experimental results show that the two criteria mutually affect each other and can be controlled by varying the control parameters of the changing functions.

Alignment-Free Cancelable Fingerprint Templates Based on Local Minutiae Information

Dynamic variable is introduced to static signature images with DRT reconstruction.Feature data length is reduced through linear compression (PCA).PNN is feasible to filter the skilled forgery from the genuine signature.Time complexity is decreased notably with one-training-sample scheme. Image-based handwritten signature verification is important in most of the financial transactions when a hard copy of signature is needed. Considering the lack of dynamic information from static signature images, we proposed a working framework through hybrid methods of discrete Radon transform (DRT), principal component analysis (PCA) and probabilistic neural network (PNN). The proposed framework aims to distinguish forgeries from genuine signatures based on the image level. Extensive experiments are conducted on our own independent signature database, and a public signature database - MYCT. Equal error rates (EER) of 1.51%, 3.23% and 13.07% are reported, respectively, for random, casual and skilled forgeries of our own database. When working on the MYCT signature database, our proposed approach manages to achieve an EER of 9.87% with 10 training samples.

Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network

Palmprint verification is an approach for verifying a palmprint input by matching the input to the claimed identity template stored in a database. If the dissimilarity measure between the input and the claimed template is below the predefined threshold value, the palmprint input is verified possessing same identity as the claimed identity template. This paper introduces an experimental evaluation of the effectiveness of utilizing three well known orthogonal moments, namely Zernike moments, pseudo Zernike moments and Legendre moments, in the application of palmprint verification. Moments are the most commonly used technique in character feature extraction. The idea of implementing orthogonal moments as palmprint feature extractors is prompted by the fact that principal features of both character and palmprint are based on line structure. These orthogonal moments are able to define statistical and geometrical features containing line structure information about palmprint. An experimental study about verification rate of the palmprint authentication system using these three orthogonal moments as feature descriptors has been discussed here. Experimental results show that the performance of the system is dependent on the moment order as well as the type of moments. The orthogonal property of these moments is able to characterize independent features of the palmprint image and thus have minimum information redundancy in a moment set. Pseudo Zernike moments of order of 15 has the best performance among all the moments. Its verification rate is 95.75%, which also represents the overall performance of this palmprint verification system.

/pdf/palmprint-verification-with-moments-tkvvchh377.pdf

Palmprint verification with moments

This paper introduces a novel discriminant moment-based method as a feature extraction technique for face recognition. In this method, pseudo Zernike moments are performed before the application of Fisher’s Linear Discriminant to achieve a stable numerical computation and good generalization in small-sample-size problems. Fisher’s Linear Discriminant uses pseudo Zernike moments to derive an enhanced subset of moment features by maximizing the between-class scatter, while minimizing the within-class scatter, which leads to a better discrimination and classification performance. Experimental results show that the proposed method achieves superior performance with a recognition rate of 97.51% in noise free environment and 97.12% in noise induced environment for the Essex Face94 database. For the Essex Face95 database, the recognition rates obtained are 91.73% and 90.30% in noise free and noise induced environments, respectively. ACM Classification: I.5.4. (Computer Methodologies-Pattern Recognition – Applications)

A Discriminant Pseudo Zernike Moments in Face Recognition

Touch-less fingerprint recognition is regarded as a viable alternative to contact-based fingerprint recognition technology. It provides a near ideal solution to the problems in terms of hygienic, maintenance and latent fingerprints. In this paper, we present a touch-less fingerprint recognition system by using a digital camera. Specifically, we address the constraints of the fingerprint images that were acquired with digital camera, such as the low contrast between the ridges and the valleys in fingerprint images, defocus and motion blurriness. The system comprises of preprocessing, feature extraction and matching stages. The proposed preprocessing stage shows the promising results in terms of segmentation, enhancement and core point detection. Feature extraction is done by Gabor filter and the favorable verification results are attained with the Support Vector Machine.

Touch-less Fingerprint Recognition System

Touch-less fingerprint recognition deserves increasing attention as it lets off the problems of deformation, maintenance, latent fingerprint problems and so on that still exist in the touch-based fingerprint technology. However, problems such as the low ridges-valleys contrast in the fingerprint images, defocus and motion blurriness raise when developing a digital camera based fingerprint recognition system. The system comprises of preprocessing, feature extraction and matching stages. The proposed preprocessing stage presents the promising results in terms of segmentation, enhancement and core point detection. Feature extraction is done by Gabor filter followed by principle component analysis (PCA) and the favorable verification results are attained with Cosine Angle.

Ying Han Pang

Papers

Image-based handwritten signature verification using hybrid methods of discrete Radon transform, principal component analysis and probabilistic neural network

Palmprint verification with moments

A Discriminant Pseudo Zernike Moments in Face Recognition

Touch-less Fingerprint Recognition System

Digital camera based fingerprint recognition