FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial
  Temporal Network
Geetanjali Sharma Gaurav Jaswal Aditya Nigam Raghavendra Ramachandra
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.