Abstract
The detection of face region can be divided into two kinds: frontal and non-frontal faces. This thesis focuses on the detection of human face region in non-frontal cases. A method of separating face and neck region is presented to extract the non-frontal face in the image. Facial features are usually used in frontal face detection, such as eyes, mouth and etc. With complete facial features, the frontal face can be easier to detected with high accuracy now. However, the research on non-frontal face detection is just beginning. Since the non frontal face image can not provide complete facial features information, it is necessary to develop a new method. Skin color is the most prominent facial feature in the non-frontal cases. It is found that the skin color has better clustering capability in YCbCr color space. According to the skin color characteristics and illumination conditions in the YCbCr color space, the Gaussian model and the Otsu method are used to segment the skin color to extract the non-frontal face region in the images. But the segmented skin color area often contains the neck region. In this paper, the contour line of the chin is fitted by illumination intensity and position information, remove the neck area and get a face region without the neck. Simulation results show the effectiveness of the proposed method for the detection of non-frontal face region.
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Wang, H., Shen, T. (2017). Research on Non-frontal Face Detection Method Based on Skin Color and Region Segmentation. In: Fei, M., Ma, S., Li, X., Sun, X., Jia, L., Su, Z. (eds) Advanced Computational Methods in Life System Modeling and Simulation. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 761. Springer, Singapore. https://doi.org/10.1007/978-981-10-6370-1_5
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DOI: https://doi.org/10.1007/978-981-10-6370-1_5
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