2024/03/24 by Geetanjali Sharma, Gaurav Jaswal, Sharma, Geetanjali +5 · 1 citation
Computer Science · #Biometric Identification and Security #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Gaze Tracking and Assistive Technology
paper · pdf · doi:10.48550/arxiv.2403.16202
openalex publication_date 2024/03/24 · openalex created_date 2024/03/27 · openalex updated_date 2026/07/28
Biometric authentication, which utilizes contactless features, such as forehead patterns, has become increasingly important for identity verification and access management. The proposed method is based on learning a 3D spatio-spatial temporal convolution to create detailed pictures of forehead patterns. We introduce a new CNN model called the Forehead Spatio-Spatial Temporal Network (FH-SSTNet), which utilizes a 3D CNN architecture with triplet loss to capture distinguishing features. We enhance the model's discrimination capability using Arcloss in the network's head. Experimentation on the Forehead Creases version 1 (FH-V1) dataset, containing 247 unique subjects, demonstrates the superior performance of FH-SSTNet compared to existing methods and pre-trained CNNs like ResNet50, especially for forehead-based user verification. The results demonstrate the superior performance of FH-SSTNet for forehead-based user verification, confirming its effectiveness in identity authentication.