Unsupervised learning of overcomplete face descriptors

The current state-of-the-art indicates that a very discriminative unsupervised face representation can be constructed by encoding overlapping multi-scale face image patches at facial landmarks. If fixed as such, there are even suggestions (albeit subtle) that the underlying features may no longer have as much meaning. In spite of the effectiveness of this strategy, we argue that one may still afford to improve especially at the feature level. In this paper, we investigate the role of overcompleteness in features for building unsupervised face representations. In our approach, we first learn an overcomplete basis from a set of sampled face image patches. Then, we use this basis to produce features that are further encoded using the Bag-of-Features (BoF) approach. Using our method, without an extensive use of facial landmarks, one is able to construct a single-scale representation reaching state-of-the-art performance in face recognition and age estimation following the protocols of LFW, FERET, and Adience benchmarks. Furthermore, we make several interesting findings related, for example, to the positive impact of applying soft feature encoding scheme preceding standard dimensionality reduction. To this end, making the encoding faster, we propose a novel method for approximative soft-assignment which we show to perform better than its hard-assigned counterpart.