Face Detection Using Adaboost and Template Matching of Depth Map based Block Rank Patterns

Abstract

성능을 Kinect sensor로 취득한 실제 영상으로 실험하였다. 실험 결과 true positive를 잘 보존하면서 많은 false positive들을 효과적으 로 제거하는 것을 보여준다. AbstractA face detection algorithms using two-dimensional (2-D) intensity or color images have been studied for decades. Recently, with the development of low-cost range sensor, three-dimensional (3-D) information (i.e., depth image that represents the distance between a camera and objects) can be easily used to reliably extract facial features. Most people have a similar pattern of 3-D facial structure. This paper proposes a face detection method using intensity and depth images. At first, adaboost algorithm using intensity image classifies face and nonface candidate regions. Each candidate region is divided into 5×5 blocks and depth values are averaged in each block. Then, 5×5 block rank pattern is constructed by sorting block averages of depth values. Finally, candidate regions are classified as face and nonface regions by matching the constructed depth map based block rank patterns and a template pattern that is generated from training data set. For template matching, the 5×5 template block rank pattern is prior constructed by averaging block ranks using training data set. The proposed algorithm is tested on real images obtained by Kinect range sensor. Experimental results show that the proposed algorithm effectively eliminates most false positives with true positives well preserved. Other face detection approach is template matching, in which the template (constructed from training set) and a pattern of candidate region (detected from input image) are compared to classify face and nonface. Various algorithms using template matching have been studied. Predefined fixed-size windows were used as templates to detect facial features [6]. Then, the template matching algorithm using variable size window that can detect faces of varying sizes was presented [7]. To construct a template of accurate face region, Kherchaoui and Houacine [8] proposed a template matching algorithm using a model of skin color with constraints. Template matching algorithms using skin information and lines of face template was also presented [9].Liu et al. [10] proposed a template matching algorithm using radial template to detect rotated face in any orientation.Jeng et al. [11] proposed a template matching algorithm using a geometrical face model using relative distance be- Finally, each candidate region is classified as a face and nonface region by matching the constructed depth map based block rank patterns and a template that is generated from training data set. For template matching, the 5×5 template block rank pattern is a priori constructed by averaging block ranks of training data set. To obtain training and test data set, we use Kinect range sensor [21]. Infrared projector projects infrared patterns that people cannot see.Infrared image is obtained by infrared camera and used to generate depth map. However, the regions that are not projected by occlusion have no depth data.The rest of the paper is structured as follows. Sec...