Abstract
The defects detection of facial skin plays a key role for the evaluation of skin health and cosmetic effect. Two typical defects, color spots and wrinkle, are detected by using their probability distribution from facial digital picture. The color spots have dominant characteristics relative to the normal skin in the corresponding color space. A new Cr-Angle A-H color space is constructed, and histogram segmentation of spots region in this space is applied by using its Poisson distribution characteristics in the facial region. Wrinkles have unique orientation and texture morphological features in facial region. The maximum filter response image and texture direction field are calculated by using Gabor filters, then Gaussian Mixture Model is used to calculate the probability value of each data vector to segment wrinkle area according to the different probability distribution. The experimental results verify the validity and accuracy of the proposed algorithm for skin defect detection.
This work presented in the paper is partially supported by the Natural Science Foundation of Tianjin (Grant No. 16JCYBJC42000).
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Ren, X., Qi, X., Wang, Z. (2019). Exploring a Facial Defect Skin Detection Algorithm with Probability Distribution Model. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_26
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DOI: https://doi.org/10.1007/978-981-13-9917-6_26
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