Gender Recognition from Face Images Using a Fusion of SVM Classifiers

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)

Abstract

The recognition of gender from face images is an important application, especially in the fields of security, marketing and intelligent user interfaces. We propose an approach to gender recognition from faces by fusing the decisions of SVM classifiers. Each classifier is trained with different types of features, namely HOG (shape), LBP (texture) and raw pixel values. For the latter features we use an SVM with a linear kernel and for the two former ones we use SVMs with histogram intersection kernels. We come to a decision by fusing the three classifiers with a majority vote. We demonstrate the effectiveness of our approach on a new dataset that we extract from FERET. We achieve an accuracy of 92.6 %, which outperforms the commercial products Face++ and Luxand.

Keywords

Gender recognition HOG LBP Histogram intersection 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.University of MaltaMsidaMalta
  2. 2.University of SalernoFiscianoItaly

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