Deep learning approach for Touchless Palmprint Recognition based on Alexnet and Fuzzy Support Vector Machine

Due to stable and discriminative features, palmprint-based biometrics has been gaining popularity in recent years. Most of the traditional palmprint recognition systems are designed with a group of hand-crafted features that ignores some additional features. For tackling the problem described above, a Convolution Neural Network (CNN) model inspired by Alex-net that learns the features from the ROI images and classifies using a fuzzy support vector machine is proposed. The output of the CNN is fed as input to the fuzzy Support vector machine. The CNN\u27s receptive field aids in extracting the most discriminative features from the palmprint images, and Fuzzy SVM results in a robust classification. The experiments are conducted on popular contactless datasets such as IITD, POLYU2, Tongji, and CASIA databases. Results demonstrate our approach outperformers several state-of-art techniques for palmprint recognition. Using this approach, we obtain 99.98% testing accuracy for the Tongji dataset and 99.76 % for the POLYU-II datasets

Palmprint Gender Classification Using Deep Learning Methods

Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and data augmentation were performed, various convolutional and deep learning-based classification approaches were empirically designed, optimized, and tested. Results of gender classification as high as 94.87% were achieved on the PolyU palmprint database and 90.70% accuracy on the CASIA palmprint database. Optimal performance was achieved by combining two different pre-trained and fine-tuned deep CNNs (VGGNet and DenseNet) through score level average fusion. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was also implemented to ascertain which specific regions of the palmprint are most discriminative for gender classification

Palmprint Identification Based on Generalization of IrisCode

The development of accurate and reliable security systems is a matter of wide interest, and in this context biometrics is seen as a highly effective automatic mechanism for personal identification. Among biometric technologies, IrisCode developed by Daugman in 1993 is regarded as a highly accurate approach, being able to support real-time personal identification of large databases. Since 1993, on the top of IrisCode, different coding methods have been proposed for iris and fingerprint identification. In this research, I extend and generalize IrisCode for real-time secure palmprint identification. PalmCode, the first coding method for palmprint identification developed by me in 2002, directly applied IrisCode to extract phase information of palmprints as features. However, I observe that the PalmCodes from the different palms are similar, having many 45o streaks. Such structural similarities in the PalmCodes of different palms would reduce the individuality of PalmCodes and the performance of palmprint identification systems. To reduce the correlation between PalmCodes, in this thesis, I employ multiple elliptical Gabor filters with different orientations to compute different PalmCodes and merge them to produce a single feature, called Fusion Code. Experimental results demonstrate that Fusion Code performs better than PalmCode. Based on the results of Fusion Code, I further identify that the orientation fields of palmprints are powerful features. Consequently, Competitive Code, which uses real parts of six Gabor filters to estimate the orientation fields, is developed. To embed the properties of IrisCode, such as high speed matching, in Competitive Code, a novel coding scheme and a bitwise angular distance are proposed. Experimental results demonstrate that Competitive Code is much more effective than other palmprint algorithms. Although many coding methods have been developed based on IrisCode for iris and palmprint identification, we lack a detailed analysis of IrisCode. One of the aims of this research is to provide such analysis as a way of better understanding IrisCode, extending the coarse phase representation to a precise phase representation, and uncovering the relationship between IrisCode and other coding methods. This analysis demonstrates that IrisCode is a clustering process with four prototypes; the locus of a Gabor function is a two-dimensional ellipse with respect to a phase parameter and the bitwise hamming distance can be regarded as a bitwise angular distance. In this analysis, I also point out that the theoretical evidence of the imposter binomial distribution of IrisCode is incomplete. I use this analysis to develop a precise phase representation which can enhance iris recognition accuracy and to relate IrisCode and other coding methods. By making use of this analysis, principal component analysis and simulated annealing, near optimal filters for palmprint identification are sought. The near optimal filters perform better than Competitive Code in term of d’ index. Identical twins having the closest genetics-based relationship are expected to have maximum similarity in their biometrics. Classifying identical twins is a challenging problem for some automatic biometric systems. Palmprint has been studied for personal identification for many years. However, genetically identical palmprints have not been studied. I systemically examine Competitive Code on genetically identical palmprints for automatic personal identification and to uncover the genetically related palmprint features. The experimental results show that the three principal lines and some portions of weak lines are genetically related features but our palms still contain rich genetically unrelated features for classifying identical twins. As biometric systems are vulnerable to replay, database and brute-force attacks, such potential attacks must be analyzed before they are massively deployed in security systems. I propose projected multinomial distribution for studying the probability of successfully using brute-force attacks to break into a palmprint system based on Competitive Code. The proposed model indicates that it is computationally infeasible to break into the palmprint system using brute-force attacks. In addition to brute-force attacks, I address the other three security issues: template re-issuances, also called cancellable biometrics, replay attacks, and database attacks. A random orientation filter bank (ROFB) is used to generate cancellable Competitive Codes for templates re-issuances. Secret messages are hidden in templates to prevent replay and database attacks. This technique can be regarded as template watermarking. A series of analyses is provided to evaluate the security levels of the measures

Palmprint Identification Based on Generalization of IrisCode

The development of accurate and reliable security systems is a matter of wide interest, and in this context biometrics is seen as a highly effective automatic mechanism for personal identification. Among biometric technologies, IrisCode developed by Daugman in 1993 is regarded as a highly accurate approach, being able to support real-time personal identification of large databases. Since 1993, on the top of IrisCode, different coding methods have been proposed for iris and fingerprint identification. In this research, I extend and generalize IrisCode for real-time secure palmprint identification. PalmCode, the first coding method for palmprint identification developed by me in 2002, directly applied IrisCode to extract phase information of palmprints as features. However, I observe that the PalmCodes from the different palms are similar, having many 45o streaks. Such structural similarities in the PalmCodes of different palms would reduce the individuality of PalmCodes and the performance of palmprint identification systems. To reduce the correlation between PalmCodes, in this thesis, I employ multiple elliptical Gabor filters with different orientations to compute different PalmCodes and merge them to produce a single feature, called Fusion Code. Experimental results demonstrate that Fusion Code performs better than PalmCode. Based on the results of Fusion Code, I further identify that the orientation fields of palmprints are powerful features. Consequently, Competitive Code, which uses real parts of six Gabor filters to estimate the orientation fields, is developed. To embed the properties of IrisCode, such as high speed matching, in Competitive Code, a novel coding scheme and a bitwise angular distance are proposed. Experimental results demonstrate that Competitive Code is much more effective than other palmprint algorithms. Although many coding methods have been developed based on IrisCode for iris and palmprint identification, we lack a detailed analysis of IrisCode. One of the aims of this research is to provide such analysis as a way of better understanding IrisCode, extending the coarse phase representation to a precise phase representation, and uncovering the relationship between IrisCode and other coding methods. This analysis demonstrates that IrisCode is a clustering process with four prototypes; the locus of a Gabor function is a two-dimensional ellipse with respect to a phase parameter and the bitwise hamming distance can be regarded as a bitwise angular distance. In this analysis, I also point out that the theoretical evidence of the imposter binomial distribution of IrisCode is incomplete. I use this analysis to develop a precise phase representation which can enhance iris recognition accuracy and to relate IrisCode and other coding methods. By making use of this analysis, principal component analysis and simulated annealing, near optimal filters for palmprint identification are sought. The near optimal filters perform better than Competitive Code in term of d’ index. Identical twins having the closest genetics-based relationship are expected to have maximum similarity in their biometrics. Classifying identical twins is a challenging problem for some automatic biometric systems. Palmprint has been studied for personal identification for many years. However, genetically identical palmprints have not been studied. I systemically examine Competitive Code on genetically identical palmprints for automatic personal identification and to uncover the genetically related palmprint features. The experimental results show that the three principal lines and some portions of weak lines are genetically related features but our palms still contain rich genetically unrelated features for classifying identical twins. As biometric systems are vulnerable to replay, database and brute-force attacks, such potential attacks must be analyzed before they are massively deployed in security systems. I propose projected multinomial distribution for studying the probability of successfully using brute-force attacks to break into a palmprint system based on Competitive Code. The proposed model indicates that it is computationally infeasible to break into the palmprint system using brute-force attacks. In addition to brute-force attacks, I address the other three security issues: template re-issuances, also called cancellable biometrics, replay attacks, and database attacks. A random orientation filter bank (ROFB) is used to generate cancellable Competitive Codes for templates re-issuances. Secret messages are hidden in templates to prevent replay and database attacks. This technique can be regarded as template watermarking. A series of analyses is provided to evaluate the security levels of the measures

A Multimodal and Multi-Algorithmic Architecture for Data Fusion in Biometric Systems

Software di autenticazione basato su tratti biometric

Palmprint biometric data acquisition: extracting a consistent Region of Interest (ROI) for method evaluation

Traditionally personal identification was based on possessions. This could be in the form of a physical key, ID card, passport, or some kind of knowledge based entry system such as a password. All of these are prone to attack where impersonation of your identity for some kind of immediate financial gain, or the more serious identity theft, is possible simply by being in physical possession of an identity device or knowledge of a password. In contrast biometric identification attempts to identify who you are. Iris or retina patterns, palmprint, fingerprint, face and voice recognition are well known examples of biometric attributes. Some biometrics such as fingerprints were established in the latter 19th century well before computers were commonplace. Others such as face, iris and voice recognition have emerged as computer technology and methodologies have developed. More recent research has also devoted attention to internal physiological biometrics based on brain (electroencephalogram), heart activity (electrocardiogram) and palm vein patterns. Even your personal gait based on how you walk has been investigated. Both security and forensic applications compete to find the best identification method trading off accuracy for performance depending on the intended application. This thesis is a continuation of previous research to develop a tool for distributed palmprint image data gathering. This would enable researchers to concentrate on method evaluation whilst not losing valuable time in data validation. This simple tool will enable palmprint biometric diversity across continents to be gathered. This thesis continues by establishing how to extract a consistent region of interest in the acquired palmprint images from a mobile phone ,or statically mounted digital, camera. The importance of establishing a consistent region of interest is considered by studying a simple existing identification method applied to a known palmprint database. In the discussions and conclusions the usefulness of this method is established and the final research outlined that is needed to finalize the palmprint acquisition tool for academic research

Evidences of Equal Error Rate Reduction in Biometric Authentication Fusion

Multimodal biometric authentication (BA) has shown perennial successes both in research and applications. This paper casts a light on why BA systems can be improved by fusing opinions of different experts, principally due to diversity of biometric modalities, features, classifiers and samples. These techniques are collectively called variance reduction (VR) techniques. A thorough survey was carried out and showed that these techniques have been employed in one way or another in the literature, but there was no systematic comparison of these techniques, as done here. Despite the architectural diversity, we show that the improved classification result is due to reduced (class-dependent) variance. The analysis does not assume that scores to be fused are uncorrelated. It does however assume that the class-dependent scores have Gaussian distributions. As many as 180 independent experiments from different sources show that such assumption is acceptable in practice. The theoretical explanation has its root in regression problems. Our contribution is to relate the reduced variance to a reduced classification error commonly used in BA, called Equal Error Rate. In addition to the theoretical evidence, we carried out as many as 104 fusion experiments using commonly used classifiers on the XM2VTS multimodal database to measure the gain due to fusion. This investigation leads to the conclusion that different ways of exploiting diversity incur different hardware and computation cost. In particular, higher diversity incurs higher computation and sometimes hardware cost and vice-versa. Therefore, this study can serve as an engineering guide to choosing a VR technique that will provide a good trade-off between the level of accuracy required and its associated cost

Biometrics

Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

Fusion of face and iris biometrics in security verification systems.

Master of Science in Computer Science. University of KwaZulu-Natal, Durban, 2016.Abstract available in PDF file