  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.
