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

arXiv: https://arxiv.org/abs/2403.16202 · HTML: https://mlsanity.com/paper/2403.16202/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 24 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Resnet in Resnet: Generalizing Residual Architectures](https://mlsanity.com/paper/1603.08029/) (2016)
- [PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features](https://mlsanity.com/paper/1812.06271/) (2018)

## Built on by (most cited first, 3 of 3)

- [Synthetic Forehead-creases Biometric Generation for Reliable User Verification](https://mlsanity.com/paper/2408.15693/) (2024)
- [Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial Curves](https://mlsanity.com/paper/2501.13889/) (2025)
- [CLRecogEye : Curriculum Learning towards exploiting convolution features for Dynamic Iris Recognition](https://mlsanity.com/paper/2511.21097/) (2025)

## Abstract (arXiv)

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.