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In this paper, we propose a novel gender recognition framework based on a Fuzzy Inference System (FIS). Our main objective is to study the gain brought by FIS in presence of various visual sensors (e.g., hair, mustache, inner face). We use inner and outer facial features to extract input variables. First, we define the fuzzy statements and then we generate a knowledge base composed of a set of rules over the linguistic variables including hair volume, mustache and a vision-sensor. Hair volume and mustache information are obtained from Part Labels subset of Labeled Faces in the Wild (LFW) database and vision-sensor is obtained from a pixel-intensity based SVM+RBF classifier trained on di↵erent databases including Feret, Groups and GENKI-4K. Cross-database test experiments on LFW database showed that the proposed method provides better accuracy than optimized SVM+RBF only classification. We also showed that FIS increases the inter-class variability by decreasing False Negatives (FN) and False Positives (FP) using expert knowledge. Our experimental results yield an average accuracy of 93.35% using Groups/LFW test, while the SVM performance baseline yields 91.25% accuracy.

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This paper presents cross-database evaluations of automatic appearance-based gender recognition methodology using normalized raw pixels and SVM classifier under unconstrained settings. Proposed method uses both histogram specification and feature space normalization on automatically aligned faces to achieve reliable recognition rate for real scenarios. Using a web based unconstrained training database, we applied local window search to increase generalization ability of the proposed method. Our contribution is two-fold. First we showed that aligned and normalized raw pixel intensities are providing the best performance in case of unconstrained cross-database tests than feature-based studies on unaligned faces. Second, we showed that histogram specification provides better normalization than that of histogram equalization for automatically aligned faces in large databases for gender recognition. Variety of cross-database experiments performed on uncontrolled Image of Groups (88.16%), Genki-4K (91.07%) and LFW databases (91.87%) showed that proposed method provides superior generalization ability than that of the state-of-the-art methods.

International audienc