人脸识别介绍_IntroFaceDetectRecognition.ppt

An Introduction to Face Detection and Recognition Ziyou Xiong Dept. of Electrical and Computer Engineering, Univ. of Illinois at Urbana-Champaign Outline Face Detection What is face detection? Importance of face detection Current state of research Different approaches One example Face Recognition What is face recognition? Its applications Different approaches One example A Video Demo What is Face Detection? Given an image, tell whether there is any human face, if there is, where is it(or where they are). Importance of Face Detection The first step for any automatic face recognition system system First step in many Human Computer Interaction systems Expression Recognition Cognitive State/Emotional State Recogntion First step in many surveillance systems Tracking: Face is a highly non rigid object A step towards Automatic Target Recognition(ATR) or generic object detection/recognition Video coding…… Face Detection: current state State-of-the-art: Front-view face detection can be done at 15 frames per second on 320x240 black-and-white images on a 700MHz PC with ~95% accuracy. Detection of faces is faster than detection of edges! Side view face detection remains to be difficult. Face Detection: challenges Out-of-Plane Rotation: frontal, 45 degree, profile, upside down Presence of beard, mustache, glasses etc Facial Expressions Occlusions by long hair, hand In-Plane Rotation Image conditions: Size Lighting condition Distortion Noise Compression Different Approaches Knowledge-based methods: Encode what constitutes a typical face, e.g., the relationship between facial features Feature invariant approaches: Aim to find structure features of a face that exist even when pose, viewpoint or lighting conditions vary Template matching: Several standard patterns stored to describe the face as a whole or the facial features separately Appearance-based methods: The models are learned from a set of training images that capture the representative variability of faces. Knowledge-