FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial Temporal Network
This is an incremental improvement for biometric authentication systems, specifically for identity verification and access management using forehead patterns.
The paper tackles user verification by using forehead creases as a biometric feature, proposing FH-SSTNet, a 3D CNN model with triplet and Arcloss, which outperforms existing methods like ResNet50 on a dataset of 247 subjects.
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.