Skin Color Transfer. Introduction. Other-Race Effect. This work

Transcription

1 Skin Color Transfer This work Introduction Investigates the importance of facial skin color for race differentiation Explore the potential information for other-race race effect Basic Idea Match the average pixel based intensity values of two images and transfer the entire color mood of the source image to the target image Facial multi-region subdivision Color fine tune Other-Race Effect A psychological discovery that other-race race faces are perceived to be more alike and less discriminable than own-race faces Explanations Psychosocial Contact Hypothesis In-group/out-group theory Psychophysical loss of facial details with decreased reflectance from dark skin race-related related differences in variability of facial features

2 Skin Color vs. Race Recognition Provide a tool for race-related related psychological research Transfer skin color from one race to the other Focus on human faces -- the most important to differentiate people of different races Application Medical surgical plan Arts design Security Law enforcement Properties of Human Skin Skin BRDF depends on pigmentation (melanin and hemoglobin), oiliness and dryness Skin color is created by a combination of blood (red) and melanin (yellow, brown) Restricted range of hues More deeply colored skin is created by adding melanin The range of possible hues shifts toward yellow as saturation increases Little texture Previous Work Color Transfer Between Images [7] Match the three-dimensional distribution of color values lab Transferring Color to Grayscale Images [8] Advantage: Simple Minimized human interference Disadvantage: High computational cost Poor performance for human faces

3 General Flow Chart HIS Color Space Transformation Facial Multi-region Subdivision Average pixel based Intensity Matching RGB Transfer Source image sample pixel Target image pixel Intensity Matching N Y Color Fine Tune Algorithm Step 1: HSI color space transformation Step 2: Skin color transfer Facial multi-region subdivision Step 3: Average pixel based intensity matching and RGB transfer Step 4: Color fine tune Average Pixel Based Intensity Matching and RGB Transfer Average pixel based method Take the color transition information into consideration Match intensity channel to find minimum Euclidian distance RGB transfer Limit sample pixels to reduce computational time

4 Color Fine Tune Properties of Lips H channel is the dominant factor Hue values usually range between 350 to 360 and 0 to 10 Local region growing based on Hue Block-based Transfer & Color Fine Tune Experimental Results Black Asian Transferred

5 Experimental Results (cont.) Asian Black Transferred Experimental Results (cont.) White Asian Transferred Experimental Results (cont.) Asian White Transferred

6 Histogram Analysis R Channel: Source Target Result Histogram Analysis (cont.) G Channel: Source Target Result Histogram Analysis (cont.) B Channel: Source Target Result

7 Psychophysical Study Test-I Each viewer was given 9 original facial images (including 3 whites, 3 blacks and 3 east-asians), and was asked to read the same set of face images in a week interval Test-II Each viewer was given 9 color-transferred facial images, including 3 artificial-whites which were generated by our skin color-transfer process, and was asked to read the same set of artificial-face face images in a week interval. Psychophysical Study (cont.) Test I (Original) Own-race/color correct-recognition recognition ratio 83.3% Other-race/color race/color false-recognition ratio 91.7% Test II (Color- transferred) 50% 58.3% Problematic Cases Asian White Transferred Black White Transferred

8 Problematic Cases (cont.) Reason: Vast contrast of intensity between source and target images (e.g. black vs. white, dark yellow vs. bright white) Comparison with Other Research White Transferred (our method) Asian Transferred ([8] s method) References [1] Robert L. Goldstone. Do We All Look Alike to Computers, Trends in Cognitive Science, pp. 55, Vol. 7, No. 2, Feb [2] Squire, C., Newhouse, J., Racial Effects in Sentencing: The Influence of Facial Features and Skin Tone, UW-L L Journal of Undergraduate Research VI, [3] Chance, J, Goldstein, A. G., Race-related variation of facial features: Anthropometric data I, Bulletin of the Psychonomic Society,, vol 13. no. 3, pp , 190, 1979 [4] A. Haro and B. Guenter and I. Essa,, Real-time photo-realistic, physically based rendering of fine scale human skin structure, Proc. 12th Eurographics E Workshop on Rendering, [5] N. Tsumura, et al, Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin, SIGGRAPH2003, p [6] D.Forsyth and M.Fleck. Finding naked people. In Proc. of European Conference of Computer Vision. Berlin, Germany: SpringerVerlag, , [7] Reinhard, E. Ashikhmin, M. Gooch B. and Shirley, P., Color Transfer T Between Images, IEEE Computer Graphics and Applications,, September /October 2001, pp [8] Welsh T., Ashikhmin M., Mueller K. Transferring Color to Grayscale Images, TOG special issue on ACM SIGGRAPH 2002, v.20. no. 3, pp