Abstract

Biometric technology protects valuable assets and digital data by utilizing human physical and behavioral traits. Among these traits, palmprint recognition is recognized as particularly effective due to its unique characteristics. This paper introduces a new method called the Hybrid Approach of Compressed Contour Texture Analysis for Palmprint Recognition System with Supervised Deep Learning Classifier (HCCTA-SDLCNet) to enhance security in biometric systems. This method integrates the Hybrid approach of Compressed Contour Texture Analysis (HCCTA) for attribute extraction with the Supervised Deep Learning Classifier (SDLCNet). The process begins with the data normalization of a Two Dimensional Palmprint Region of Interest (2DPROI), followed by the capture of the Contour Pre-Processed Image of 2D-PROI Sample (CPI) using the Progressing Amalgamation of Conventional Compression (PACC) and the Canny edge detection model. The PACC combines the Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) for sample compression, final in a Compressed Contour Preprocessed 2DPROI Image (CCPI). The HCCTA method focuses on extracting essential texture features from the CCPI. These extracted features are then analyzed using the SDLCNet classification model to identify individuals. The research utilizes 2DPROI data from the POLYU database at Hong Kong Polytechnic University. The intended system demonstrates a high benchmark, achieving 99.5% recognition accuracy, which is superior to existing biometric approaches.

Authors

B. Abirami, K. Krishnaveni
Sri S. Ramasamy Naidu Memorial College, India

Keywords

Biometric Technology, Hybrid Approach of Compressed Contour Texture Analysis, Palmprint Recognition System, Two Dimensional Palmprint Region of Interest (2DPROI), Canny Edge Detection, Supervised Deep Learning Classifier, DWT, Principle Component Analysis