The Research Of Multi-pose Face Detection Based On Improved Adaboost Algorithm

Face detection is the process of determining the input image all face position, size, number, and pose. With the development of electronic technology and the advent of the information age, the intelligent equipment constantly emerging, human-computer interaction has developed to a new height, face detection has the attention of the people long time as a special case of object detection problem. Some discussions about the multi-pose face detection based on improved Adaboost and Camshift face tracking combined with Kalman filter have been studied in this paper, the mainly work for the following respects:(1) Studied distribution of skin color in color spaces and division technology. In this paper, ten collected face images have been projected to RGB, YIQ, YCbCr color space, the cluster of skin color samples has been estimated, and then four image segmentation algorithms have been used to segment the test image and computed the error rate of segmentation, got the ROI through histogram projection. Experiments showed that, threshold segmentation algorithm based on YCbCr color space could be simply realized and had the high segmentation accuracy rate.(2) Studied the multi-pose face detection based on skin color and Adaboost of improved image pyramid technology. In order to improve the detection rate of profile face, we added the new Haar-like features representing the edge of face image; had made improvement the image pyramid structure of image based on Adaboost, used image resize algorithm based on content aware to construct two layers image pyramid, in detection only need to detect the bottom of image pyramid, simplified the structure of the image pyramid, shorted the time of constructing image pyramid. Before face detection using Adaboost algorithm, used template matching roughly estimates face image, and then detected the ROI by multi-pose face detection. Experiments showed that, combined with the algorithm of skin color and Adaboost had higher detection rate with multi-pose face detection.(3) Studied Camshift automatic face tracking combined with Kalman filter, designed and realized video monitoring function of a local area network (LAN). This paper used Camshift algorithm to track face, but it needed to manually mark tracking object in initial moment, the improved Adaboost algorithm was proposed to mark the position information of face by this paper, and then combing with Camshift to tracking face. When the sheltered occurred in the tracking process, used kalman filter estimating the state of face in next frame. For multi-face tracking, multi-face tracking queue was proposed. At last, we designed and realized the video monitoring function of LAN in this paper.