Open-set Face Recognition for Small Galleries Using Siamese Networks

• URL: http://arxiv.org/abs/2105.06967v1
• Date: Fri, 14 May 2021 17:16:37 GMT
• Title: Open-set Face Recognition for Small Galleries Using Siamese Networks
• Authors: Gabriel Salomon, Alceu Britto, Rafael H. Vareto, William R. Schwartz, David Menotti
• Abstract summary: The present work introduces a novel approach towards open-set face recognition focusing on small galleries. A Siamese Network architecture is proposed to learn a model to detect if a face probe is enrolled in the gallery. State-of-the-art methods like HFCN and HPLS were outperformed on FRGCv1.
• Score: 1.8514314381314887
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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