# Open-set Face Recognition for Small Galleries Using Siamese Networks

arXiv: https://arxiv.org/abs/2105.06967 · HTML: https://mlsanity.com/paper/2105.06967/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 14 May 2021.

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

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [DeepID3: Face Recognition with Very Deep Neural Networks](https://mlsanity.com/paper/1502.00873/) (2015)
- [Neural Aggregation Network for Video Face Recognition](https://mlsanity.com/paper/1603.05474/) (2016)
- [Toward Open-Set Face Recognition](https://mlsanity.com/paper/1705.01567/) (2017)
- [Web-Scale Training for Face Identification](https://mlsanity.com/paper/1406.5266/) (2014)
- [Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines](https://mlsanity.com/paper/2005.03106/) (2020)
- [Face Recognition Using Deep Multi-Pose Representations](https://mlsanity.com/paper/1603.07388/) (2016)
- [Can Giraffes Become Birds? An Evaluation of Image-to-image Translation for Data Generation](https://mlsanity.com/paper/2001.03637/) (2020)

## Built on by (most cited first, 2 of 2)

- [TinySiamese Network for Biometric Analysis](https://mlsanity.com/paper/2307.00578/) (2023)
- [Open-Set Object Recognition Using Mechanical Properties During Interaction](https://mlsanity.com/paper/2311.01540/) (2023)

## Abstract (arXiv)

Face recognition has been one of the most relevant and explored fields of Biometrics. In real-world applications, face recognition methods usually must deal with scenarios where not all probe individuals were seen during the training phase (open-set scenarios). Therefore, open-set face recognition is a subject of increasing interest as it deals with identifying individuals in a space where not all faces are known in advance. This is useful in several applications, such as access authentication, on which only a few individuals that have been previously enrolled in a gallery are allowed. The present work introduces a novel approach towards open-set face recognition focusing on small galleries and in enrollment detection, not identity retrieval. A Siamese Network architecture is proposed to learn a model to detect if a face probe is enrolled in the gallery based on a verification-like approach. Promising results were achieved for small galleries on experiments carried out on Pubfig83, FRGCv1 and LFW datasets. State-of-the-art methods like HFCN and HPLS were outperformed on FRGCv1. Besides, a new evaluation protocol is introduced for experiments in small galleries on LFW.