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Probabilistic Modeling for Detection and Gender Classification

In this paper, the authors contribute to solve the simultaneous problems of detection and gender classification from any viewpoint. The authors use an invariant model for accurate face localization based on a combination of appearance and geometric. A probabilistic matching of visual traits allows to classify the gender of face even when pose changes. The authors deal with the local invariant features whose performances have already been proved. Each facial feature retained in the detection step will be weighted by a probability to be male or female. This feature contributes to determine the gender of the face. The authors evaluate our model by testing it in experiments on different databases. The experimental results show that the face model performs well to detect face and gives a good gender recognition rate in the presence of viewpoint changes and facial appearance variability.