Open-set Face Recognition for Small Galleries Using Siamese Networks

TL;DR

This paper presents a Siamese Network-based method for open-set face recognition in small galleries, focusing on enrollment detection rather than identity retrieval, and demonstrates superior performance over existing methods on multiple datasets.

Contribution

It introduces a novel Siamese Network approach for open-set face recognition tailored to small galleries and enrollment detection, with a new evaluation protocol for LFW.

Findings

Outperforms state-of-the-art methods like HFCN and HPLS on FRGCv1.

Achieves promising results on Pubfig83, FRGCv1, and LFW datasets.

Introduces a new evaluation protocol for small galleries on LFW.

Abstract

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.