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 Recognition in Uncontrolled and Uncooperative Environment

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.Comment: Accepted in the IEEE Transactions on Information Forensics and Securit

Contour Fractal Dimension Analysis using Square-Box ROI Extraction Approach with Convolution Neural Network Classifier for Palmprint Recognition System

Contour Fractal Dimension Analysis using Square-Box ROI Extraction Approach with Convolution Neural Network Classifier for Palmprint Recognition System (CFDCNNNet) is proposed. To bring about the originality, Contour Fractal Dimension (CFD) feature extraction approach and a Convolution Neural Network (CNNNet) classifier approach are employed. To impart the novelty the CFD feature extraction approach, Two Dimensional-Palmprint Region of Interest (2D-PROI) is captured from five different datasets using Square-Box ROI Extraction approach and point out all the edges/contours of 2D-PROI image (CPI) using Canny edge detection algorithm and then estimate the Fractal Dimension (FD) values using Box-Counting algorithm to create a distinctive feature vector. Classify this feature vector using Convolution Neural Network (CNNNet) classifier approach to identify the authorized person at a higher accuracy rate. This research explores on five different datasets such as CASIA, IITD, BMPD, SMPD and multi--spectral 2D-PROI image databases. The CFDCNNNet System model has been determined the authentication accuracy of different datasets with 98.66% of authentication accuracy