FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial Temporal Network

• Published in: 2024 12th International Workshop on Biometrics and Forensics (IWBF) pp. 1 - 6
• Main Authors: Sharma, Geetanjali, Jaswal, Gaurav, Nigam, Aditya, Ramachandra, Raghavendra
• Format: Conference Proceeding
• Language: English
• Published: IEEE 11.04.2024
• Subjects: Biometrics; Biometrics (access control); Convolution; Forehead; Forehead creases; Forensics; metric learning; Solid modeling; Spatial-Spatio Temporal; Stacking; Three-dimensional displays; Verification
• DOI: 10.1109/IWBF62628.2024.10593960

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.