A Multiple Channel Biometric Recognition Model Using Palm Images
Main Article Content
Abstract
Biometric identification technologies are playing an increasingly important role in securing authentication. Palm-based recognition is garnering significant attention due to the unique and reliable patterns, lines, and texture structures found on the human palm. This study proposes a novel palm-based biometric recognition system that utilizes state-of-the-art computer vision and deep learning techniques. Contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE) are applied to enhance the visibility of features in the preprocessed images under varying lighting conditions. To improve recognition accuracy, a hybrid deep learning architecture is designed by integrating a pretrained ResNet-based backbone with a multi-channel framework. This approach effectively merges multiple distinguishing properties of the palm, including lines and texture patterns. The model is trained and evaluated on a dataset of 25,600 palm images from 128 individuals, captured in different locations and from various angles. Experimental results demonstrate strong performance, with high accuracy, sensitivity, specificity, and recall, reflecting the robustness and reliability of the system. This work contributes to a scalable and efficient solution for palm-based biometric authentication, offering a promising approach for secure identity verification.
Keywords
Biometric recognition, CLAHE, multi-channel model, palm-based biometrics.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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