Are External Face Features Useful for Automatic Face Classification?

In this paper a new experiment using the FRGC database is proposed. The experiment deals with the use of external face features for face classification. Unlike the most part of algorithms that can be found in the literature for classifying faces, we consider the external information located at hair and ears as a reliable source of information. These features have often been discarded due to the difficulty of their extraction and alignment, and the lack of robustness in security related applications. Nevertheless, there are a lot of applications where these considerations are not valid, and the proper processing of external features can be an important additional source of information for classifications tasks. We also propose, following this assumption, a method for extracting external information from face images. The method is based on a top-down reconstructionbased algorithm for extracting the external face features. Once extracted, they are encoded in a second step using the Non Negative Matrix Factorization (NMF) algorithm, yielding an aligned high dimensional feature vector. This method has been used in a gender recognition problem, concluding that the encoded information is useful for classification purposes.