A hybrid fuzzy framework for face detection and recognition using behavioral traits

Abstract

Face recognition integrated with emotions and behavior can be a salient feature for identification of antisocial/criminal people. In this paper, a hybrid Fuzzy based Behavior Prognostic System for Disparate traits (FBPSDs) has been proposed. FBPSDs can be used in an emergency situation to identify and extract mischievous person(s) involved in any swindling act. The technique uses face recognition to identify the contextual faces. Further, fuzzy rules have been formulated to extricate more features for the culprit identification. The proposed framework has three stages: face detection, feature extraction and behavior analysis. Face detection for the proposed FBPSDs is further implemented using three techniques Haar Cascade, Skin color based and HOG methods on Labeled Faces in the Wild (LFW) and Facial dataset (Face 94, Face 95 and Face 96). Testing accuracy of 91%, 50%, 92% (LFW) and 91%, 60%, 91.5% (facial dataset) is achieved. Result analysis and evaluation prove that HOG method is the most acceptable and appropriate method for face detection.