Published Paper Details:

The integration of advanced biometrics is critical for the evolving global financial technology (FinTech) sector, which requires fast, secure, and contactless authentication. This review analyzes the deployment of palmprint recognition systems for secure payment, a modality projected for significant market expansion (CAGR of 26.10%) due to its high feature density, permanence, and non-contact nature. The paper details state-of-the-art deep learning architectures, such as the hybrid Vision Transformer (ViT) and Convolutional Neural Network (CNN) fusion utilized in the Palm-ID system, which achieves high accuracy (TAR of 98.06% at FAR=0.01%) and superior operational efficiency, including template sizes compressed to 516 bytes and sub-millisecond 1:N search times. Crucially, the review examines the robust security framework required for financial adoption, including Presentation Attack Detection (PAD) and the implementation of cancelable biometrics for template protection to ensure revocability. Finally, it addresses the mandatory compliance with international data privacy regulations, such as GDPR and CCPA, affirming that optimized palmprint recognition is technologically ready and strategically positioned to secure the next generation of frictionless retail payment systems.