MEG: Multi-Expert Gender Classification from Face Images in a Demographics-Balanced Dataset

In this paper we focus on gender classification from face images, which is still a challenging task in unrestricted scenarios. This task can be useful in a number of ways, e.g., as a preliminary step in biometric identity recognition supported by demographic information. We compare a feature based approach with two score based ones. In the former, we stack a number of feature vectors obtained by different operators, and train a SVM based on them. In the latter, we separately compute the individual scores from the same operators, then either we feed them to a SVM, or exploit likelihood ratio based on a pairwise comparison of their answers. Experiments use EGA database, which presents a good balance with respect to demographic features of stored face images. As expected, feature level fusion achieves an often better classification performance but it is also quite computationally expensive. Our contribution has a threefold value: 1) the proposed score level fusion approaches, though less demanding, achieve results which are rather similar or slightly better than feature level fusion, especially when a particular set of experts are fused; since experts are trained individually, it is not required to evaluate a complex multi-feature distribution and the training process is more efficient; 2) the number of uncertain cases significantly decreases; 3) the operators used are not computationally expensive in themselves

Recognition of Facial Attributes Using Adaptive Sparse Representations of Random Patches

Abstract. It is well known that some facial attributes –like soft bio-metric traits – can increase the performance of traditional biometric sys-tems and help recognition based on human descriptions. In addition, other facial attributes –like facial expressions – can be used in human– computer interfaces, image retrieval, talking heads and human emotion analysis. This paper addresses the problem of automated recognition of facial attributes by proposing a new general approach called Adap-tive Sparse Representation of Random Patches (ASR+). In the learning stage, random patches are extracted from representative face images of each class (e.g., in gender recognition –a two-class problem–, images of females/males) in order to construct representative dictionaries. In the testing stage, random test patches of the query image are extracted, and for each test patch a dictionary is built concatenating the ‘best ’ repre-sentative dictionary of each class. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the query image is classified by patch vot-ing. Thus, our approach is able to learn a model for each recognition task dealing with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size and distance from the camera. Experi-ments were carried out on seven face databases in order to recognize facial expression, gender, race and disguise. Results show that ASR+ deals well with unconstrained conditions, outperforming various repre-sentative methods in the literature in many complex scenarios