Face Detection Under Complex Background

Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, all of these researching directions involve in one problem- face detection and location, in other words, before this face processing, we must know f aces' locations and scales. Consequently, to build an automated face processing system which analyzes the information contained in face images, robust and efficient face detection algorithms are require.Face is one of the most important vision objects in video and digital image and provides a great deal of visual information. Hence, face detection technique has been an important research field in computer vision. Given an arbitrary image, the goal of face detection is to determine whether or not there are any faces in the image and, if present, return the image location and extent of each face. Face detection is the first step for any fully automatic face recognition system, and in many surveillance systems, it is also a step towards Automatic Target Recognition (ATR) or generic object detection/recognition.Relying on the analysis of existing face detection technique, this paper mainly describes face detection based on rectangle feature and dynamic face detection technique combined motion information. The main work can be stated next:1. Study and realize a face detection algorithm based on skin color. Among many color spaces, this paper used YCbCr components. Since in the YCbCr color space, the luminance in formation is contained in Y component and the chrominance information is in Cb and Cr. Therefore, the luminance information can be easily de-embedded. This paper proposes a face detection algorithm for color images in the presence of varying lighting conditions as well complex backgrounds. Based on a novel lighting compensation, our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye and mouth maps for verifying each face candidate. Experimental results demonstrate successful face detection over a range of facial variations in color, position, scale and expression in color image.2. Study and realize a face detection algorithm based on rectangle features. A fixed size training set is used to train weak classifier for each rectangle feature. And the AdaBoost algorithm is applied to promote the performance of weak classifiers to form strong classifiers containing many weak classifiers. Experiments show this detector can detect the face fast and exactly.3. Using VC++ to realize two face detection methods.4. A training data set has been constructed, which includes 1000 faces and 10000 non-faces, and each face is frontal, equal illumination and uncovered.