Abstract
Hair is a salient feature in human face region and are one of the important cues for face analysis. Accurate detection and presentation of hair region is one of the key components for automatic synthesis of human facial caricature. In this paper, an automatic hair detection algorithm for the application of automatic synthesis of facial caricature based on a single image is proposed. Firstly, hair regions in training images are labeled manually and then the hair position prior distributions and hair color likelihood distribution function are estimated from these labels efficiently. Secondly, the energy function of the test image is constructed according to the estimated prior distributions of hair location and hair color likelihood. This energy function is further optimized according to graph cuts technique and initial hair region is obtained. Finally, K-means algorithm and image postprocessing techniques are applied to the initial hair region so that the final hair region can be segmented precisely. Experimental results show that the average processing time for each image is about 280 ms and the average hair region detection accuracy is above 90%. The proposed algorithm is applied to a facial caricature synthesis system. Experiments proved that with our proposed hair segmentation algorithm the facial caricatures are vivid and satisfying.
Highlights
Hair region is one of the most important components in human face
We propose an efficient automatic hair segmentation algorithm by combining graph cuts optimization and K-means clustering, since the application of our proposed method is automatic facial caricature synthesis
We integrated our algorithm into an automatic facial caricature synthesis system and further experiments showed that the resulting facial caricatures are vivid and satisfying
Summary
Hair region is one of the most important components in human face. It is one of the main reasons for the different appearances among different people. For the application of facial caricature synthesis, hair region is usually extracted manually or automatically detected with a bright and homogeneous background This is because the shape of hair varies widely and the color is different among different people. Rousset and Coulon [7] estimate the initial hair region binary mask through color information and frequency domain analysis They use image matting technique in order to get the final hair region mask. For the application of facial caricature synthesis and face animation, Chen et al [8] use manually labeled hair line together with knockout image matting technique for hair detection. Wan et al [9] propose to combine image thresholding and contour tracking techniques to segment hair regions This method is very simple but could hardly deal with complex backgrounds. We integrated our algorithm into an automatic facial caricature synthesis system and further experiments showed that the resulting facial caricatures are vivid and satisfying
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