Boosting gender recognition performance with a fuzzy inference system

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 different 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.