Probabilistic Modeling for Detection and Gender Classification

Mokhtar Taffar, Serge Miguet, Mohammed Benmohammed
2014 International Journal of Computer Vision and Image Processing  
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
more » ... 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.
doi:10.4018/ijcvip.2014010103 fatcat:nu5jcjq4krflnaxvuteymw7w3i