Combining Contrast Information and Local Binary Patterns for Gender Classification Juha Ylioinas, Abdenour Hadid, and Matti Pietik¨ ainen Machine Vision Group, P.O. Box 4500, FI-90014 University of Oulu, Finland {juyl,hadid,mkp}@ee.oulu.fi Abstract. Recent developments in face analysis showed that local bi- nary patterns (LBP) provide excellent results in representing faces. LBP is by definition a purely gray-scale invariant texture operator, codify- ing only the facial patterns while ignoring the magnitude of gray level differences (i.e. contrast). However, pattern information is independent of the gray scale, whereas contrast is not. On the other hand, contrast is not affected by rotation, but patterns are, by default. So, these two measures can supplement each other. This paper addresses how well fa- cial images can be described by means of both contrast information and local binary patterns. We investigate a new facial representation which combines both measures and extensively evaluate the proposed represen- tation on the gender classification problem, showing interesting results. Furthermore, we compare our results against those of using Haar-like features and AdaBoost learning, demonstrating improvements with a significant margin. Keywords: Texture Features, Local Binary Patterns, Contrast, Gender Classification. 1 Introduction Recent developments in face analysis showed that local binary patterns (LBP) [1] provide excellent results in representing faces [2,3]. For instance, it has been successfully applied to face detection [4], face recognition [2], facial expression recognition [5], gender classification [6] etc. LBP is a gray-scale invariant texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel with the value of the center pixel and considers the result as a binary number. LBP labels can be regarded as local primitives such as curved edges, spots, flat areas etc. The histogram of the labels can be then used as a face descriptor. Due to its discriminative power and computational simplicity, the LBP methodology has already attained an established position in face analysis research 1 . LBP is by definition a purely gray-scale invariant texture operator, codifying only the facial patterns while ignoring the magnitude of gray level differences (i.e. 1 See LBP bibliography at http://www.cse.oulu.fi/MVG/LBP_Bibliography A. Heyden and F. Kahl (Eds.): SCIA 2011, LNCS 6688, pp. 676–686, 2011. c  Springer-Verlag Berlin Heidelberg 2011