A performance based study on gender recognition in large datasets

[EN]Gender recognition has achieved impressive results based on the face appearance in controlled datasets. Its application in the wild and large datasets is still a challenging task for researchers. In this paper, we make use of classical techniques to analyze their performance in controlled and uncontrolled condition respectively with the LFW and MORPH datasets. For both sets the benchmarking protocol follows the 5-fold cross-validation proposed by the BEFIT challenge.

Learning to recognize gender using experince

[EN]Automatic facial analysis abilities are commonly integrated in a system by a previous off-line learning stage. In this paper we argue that a facial analysis system would improve its facial analysis capabilities based on its own experience similarly to the way a biological system, i.e. the human system, does throughout the years. The approach described, focused on gender classification, updates its knowledge according to the classification results. The presented gender experiments suggestthatthisapproachispromising,evenwhenjustashort simulationofwhatforhumanswouldtakeyearsofacquisition experience was performed.

An analysis of automatic gender classification

[EN]Different researches suggest that inner facial features are not the only discriminative features for tasks such as person identification or gender classification. Indeed, they have shown an influence of features which are part of the local face context, such as hair, on these tasks. However, object-centered approaches which ignore local context dominate the research in computational vision based facial analysis. In this paper, we performed an analysis to study which areas and which resolutions are diagnostic for the gender classification problem. We first demonstrate the importance of contextual features in human observers for gender classification using a psychophysical ”bubbles” technique.

Gender and identity classification for a naive and evolving system

[EN]This paper does not propose a new technique for face representationorclassification. Insteadtheworkdescribed here investigates the evolution of an automatic system which, based on a currently common framework, and starting from an empty memory, modifies its classifiers according to experience. In the experiments we reproduce up to a certain extent the process of successive meetings. The results achieved, even when the number of different individuals is still reduced compared to off-line classifiers, are promising.