# TinySiamese Network for Biometric Analysis

arXiv: https://arxiv.org/abs/2307.00578 · HTML: https://mlsanity.com/paper/2307.00578/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 84 references that are in the map.
- First posted on arXiv 2 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation](https://mlsanity.com/paper/2011.01461/) (2020)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [Differential Morphed Face Detection Using Deep Siamese Networks](https://mlsanity.com/paper/2012.01541/) (2020)
- [Combining the Silhouette and Skeleton Data for Gait Recognition](https://mlsanity.com/paper/2202.10645/) (2022)
- [GaitTAKE: Gait Recognition by Temporal Attention and Keypoint-guided Embedding](https://mlsanity.com/paper/2207.03608/) (2022)
- [A Contactless Fingerprint Recognition System](https://mlsanity.com/paper/2108.09048/) (2021)
- [Open-set Face Recognition for Small Galleries Using Siamese Networks](https://mlsanity.com/paper/2105.06967/) (2021)
- [Progressive Feature Learning for Realistic Cloth-Changing Gait Recognition](https://mlsanity.com/paper/2207.11720/) (2022)
- [Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition](https://mlsanity.com/paper/2209.12364/) (2022)

## Abstract (arXiv)

Biometric recognition is the process of verifying or classifying human characteristics in images or videos. It is a complex task that requires machine learning algorithms, including convolutional neural networks (CNNs) and Siamese networks. Besides, there are several limitations to consider when using these algorithms for image verification and classification tasks. In fact, training may be computationally intensive, requiring specialized hardware and significant computational resources to train and deploy. Moreover, it necessitates a large amount of labeled data, which can be time-consuming and costly to obtain. The main advantage of the proposed TinySiamese compared to the standard Siamese is that it does not require the whole CNN for training. In fact, using a pre-trained CNN as a feature extractor and the TinySiamese to learn the extracted features gave almost the same performance and efficiency as the standard Siamese for biometric verification. In this way, the TinySiamese solves the problems of memory and computational time with a small number of layers which did not exceed 7. It can be run under low-power machines which possess a normal GPU and cannot allocate a large RAM space. Using TinySiamese with only 8 GO of memory, the matching time decreased by 76.78% on the B2F (Biometric images of Fingerprints and Faces), FVC2000, FVC2002 and FVC2004 while the training time for 10 epochs went down by approximately 93.14% on the B2F, FVC2002, THDD-part1 and CASIA-B datasets. The accuracy of the fingerprint, gait (NM-angle 180 degree) and face verification tasks was better than the accuracy of a standard Siamese by 0.87%, 20.24% and 3.85% respectively. TinySiamese achieved comparable accuracy with related works for the fingerprint and gait classification tasks.