Probabilistic Modeling for Detection and Gender Classification

Probabilistic Modeling for Detection and Gender Classification

Mokhtar Taffar (Computer Science Department, University of Jijel, Jijel, Algeria), Serge Miguet (Université Lyon, Lyon, France) and Mohammed Benmohammed (LIRE Laboratory, University of Constantine, Constantine, Algeria)
Copyright: © 2014 |Pages: 10
DOI: 10.4018/ijcvip.2014010103
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Abstract

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.
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In face image analysis, determining gender of face from facial trait classification is still a challenge task. The greater part of publications of gender classification highlights the state-of-the-art in general trait classification. Trait learning is done by using spatially global feature representations such as templates (Kim, 2006; Gutta, 1998), principal components (Moghaddam, 2002), independent components (Jain, 2005), or image intensities directly (Baluja, 2007). While most approaches utilize intensity data, 3D information may also improve sex classification (O'Toole, 1997). In this context, much works have explored the capacity of different machine learning such as neural networks (Gutta, 1998), SVMs, and boosted classifiers (Baluja, 2007). Recently, trait classification based on local features (Kadir, 2001; Lowe, 2004; Mikolajczyk, 2002; Mikolajczyk, 2004; Yu, 2009) has emerged, using local regions (BenAbdelkader, 2005) or Haar wavelets (Shakhnarovich, 2002; Yang, 2006). Local features are principally computed from the regions of image as the values of Histograms of Oriented Gradients (HOG) (Lowe, 2004). They are image descriptors in variant to 2D rotation which have been used in many different problems in computer vision.

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