Hand Gesture Detection Using Haar Classifier with Appropriate Skin Color, Kernal Sizing & Auto Thresholding

Abstract

Hand gesture detection & recognition intends to detect & recognize some meaningful hand sign. It is an utmost challenge to design such intelligent human-computer interface. 3D hand gesture recognition, one of the most advanced technologies for building smart communication method with computers, has been developing an enormous research interest in computer vision, pattern recognition and human-computer interaction (HCI) system. The developing depth sensors enormously propelled different hand motion identification methodologies and applications, which were extremely restricted in the 2D area with conventional cameras. In this paper, we provided a productive method for image preprocessing and detection of certain gesture, advance classifier theory based on genetic algorithm, boosting algorithm & Fischer’s linear discriminant algorithm. Improvising the traditional preprocessing of image for gesture detection where skin color detection, kernel matrix evolution & auto-thresholding of image are the most challenging part of this thesis paper. This paper additionally displays a review of the state-of-the-art research for 3D hand motion recognition in four perspectives: 3D hand modeling, basic sensors capable for detecting hand gesture, static hand motion acknowledgment, hand direction motion acknowledgment, continuous hand gesture recognition and related applications of hand gesture.