The fundamentals of unimodal palmprint authentication based on a biometric system: A review

Biometric system can be defined as the automated method of identifying or authenticating the identity of a living person based on physiological or behavioral traits. Palmprint biometric-based authentication has gained considerable attention in recent years. Globally, enterprises have been exploring biometric authorization for some time, for the purpose of security, payment processing, law enforcement CCTV systems, and even access to offices, buildings, and gyms via the entry doors. Palmprint biometric system can be divided into unimodal and multimodal. This paper will investigate the biometric system and provide a detailed overview of the palmprint technology with existing recognition approaches. Finally, we introduce a review of previous works based on a unimodal palmprint system using different databases

Review of Multimodal Biometric Identification Using Hand Feature and Face

In the era of Information Technology, openness of the information is a major concern. As the confidentiality and integrity of the information is critically important, it has to be secured from unauthorized access. Security refers to prohibit some unauthorized persons from some important data or from some precious assets. So we need accurateness on automatic personal identification in various applications such as ATM, driving license, passports, citizen's card, cellular telephones, voter's ID card etc. Unimodal system carries some problems such as Noise in sensed data, Intra-class variations, Inter-class similarities, Non-universality and Spoof attacks. The accuracy of system is improved by combining different biometric traits which are called multimodal. This system gives more accuracy as it would be difficult for imposter to spoof multiple biometric traits simultaneously. This paper reviews different methods for fusion of biometric traits

Multimodal Biometrics Enhancement Recognition System based on Fusion of Fingerprint and PalmPrint: A Review

This article is an overview of a current multimodal biometrics research based on fingerprint and palm-print. It explains the pervious study for each modal separately and its fusion technique with another biometric modal. The basic biometric system consists of four stages: firstly, the sensor which is used for enrolmen

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

A PIPELINED APPROACH FOR FPGA IMPLEMENTATION OF BI MODAL BIOMETRIC PATTERN RECOGNITION

ABSTRACT A Biometric system is essentially a pattern recognition system that makes use of biometric traits to recognize individuals. Systems which are built upon multiple sources of information for establishing identity which are known as multimodal biometric systems can overcome some of the limitations like noisy captured data, intra class variations etc… In this paper a Bi modal biometric system of iris and palm print based on Wavelet Packet Transform (WPT), gabor filters and a neural classifier implemented in FPGA is described. Iris is the unique observable visible feature present in the detailed texture of each eye. Palmprint is referred to the textural data like principal lines wrinkles and ridges present in the palm. The visible texture of a person's iris and palm print is encoded into a compact sequence of 2-D wavelet packet coefficients constituting a biometric signature or a feature vector code. In this paper, a novel multi-resolution approach based on WPT for recognition of iris and palmprint is proposed. With an adaptive threshold, WPT sub image coefficients are quantized into 1, 0 or -1 as biometric signature resulting in the size of biometric signature as 960 bits. The combined pattern vector of palm print features and iris features are formed using fusion at feature level and applied to the pattern classifier. The Learning Vector Quantization neural network is used as pattern classifier and a recognition rate of 97.22% is obtained. A part of the neural network is implemented for input data of 16 dimensions and 12 input classes and 8 output classes, using virtex-4 xc4vlx15 device. This system can complete recognition in 3.25 microseconds thus enabling it being suitable for real time pattern recognition tasks