A benchmark study of large-scale unconstrained face recognition
Many efforts have been made in recent years to tackle the unconstrained face recognition challenge. For the benchmark of this challenge, the Labeled Faces in theWild (LFW) database has been widely used. However, the standard LFW protocol is very limited, with only 3,000 genuine and 3,000 impostor matches for classification. Today a 97% accuracy can be achieved with this benchmark, remaining a very limited room for algorithm development. However, we argue that this accuracy may be too optimistic because the underlying false accept rate may still be high (e.g. 3%). Furthermore, performance evaluation at low FARs is not statistically sound by the standard protocol due to the limited number of impostor matches. Thereby we develop a new benchmark protocol to fully exploit all the 13,233 LFW face images for large-scale unconstrained face recognition evaluation under both verification and open-set identification scenarios, with a focus at low FARs. Based on the new benchmark, we evaluate 21 face recognition approaches by combining 3 kinds of features and 7 learning algorithms. The benchmark results show that the best algorithm achieves 41.66% verification rates at FAR=0.1%, and 18.07% open-set identification rates at rank 1 and FAR=1%. Accordingly we conclude that the large-scale unconstrained face recognition problem is still largely unresolved, thus further attention and effort is needed in developing effective feature representations and learning algorithms. We thereby release a benchmark tool to advance research in this field.