Systematic Review: Image Processing Based Dorsal Vein Pattern Biometric Authentication System

Abstract

Biometrics leverages unique biological or behavioral traits for person or object identification, characterized by traits such as uniqueness, indivisibility, immutability, and resilience against loss or theft. Evolving biometric systems include facial recognition, iris scanning, fingerprint recognition, palm line recognition, and dorsal hand vein recognition. Dorsal hand vein identification, concealed beneath the skin, provides a highly secure biometric modality resistant to forgery, tampering, or damage. This systematic literature review offers an overview of dorsal hand vein image processing for biometric authentication systems, covering recent advancements in image processing, including concept clarification, data acquisition, pre-processing, segmentation, feature extraction, and identification phases. The review spans research articles from 2018 to 2023, sourced from reputable outlets such as 208 MDPI journals, Google Scholar, IEEE Xplore, Google Books, and other scholarly repositories. Stringent article selection, guided by predefined keywords, ensures research quality. Findings emphasize the necessity of a camera and infrared LED for dorsal hand vein data acquisition to build an effective identification system. Pre-processing involves Region of Interest (ROI) selection, contrast enhancement, and intensity normalization. Segmentation employs morphological operations, filter banks, and adaptive thresholding. Feature extraction encompasses statistical features and the Gray Level Co-occurrence Matrix (GLCM). Convolutional Neural Networks (CNNs) play a pivotal role in identification, featuring layers such as Conv1_1, Conv1_2, batch normalization, max-pooling, and dropout layers.

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