Name
Abstract | ||
|---|---|---|
Little prior image processing work has addressed estimation and classification of skin color in a manner that is independent of camera and illuminant. To this end, we first present new methods for 1) fast, easy-to-use image color correction, with specialization toward skin tones, and 2) fully automated estimation of facial skin color, with robustness to shadows, specularities, and blemishes. Each of these is validated independently against ground truth, and then combined with a classification method that successfully discriminates skin color across a population of people imaged with several different cameras. We also evaluate the effects of image quality and various algorithmic choices on our classification performance. We believe our methods are practical for relatively untrained operators, using inexpensive consumer equipment. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/ICIP.2005.1530070 | 2005 International Conference on Image Processing (ICIP), Vols 1-5 |
Keywords | Field | DocType |
image classification,image processing,ground truth,image quality | Computer vision,Pattern recognition,Color histogram,Computer science,Color balance,Demosaicing,Color correction,Artificial intelligence,RGB color model,Color normalization,False color,Color image | Conference |
ISSN | Citations | PageRank |
1522-4880 | 6 | 0.74 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Harville | 1 | 369 | 35.55 |
| H. Harlyn Baker | 2 | 337 | 340.55 |
| Nina Bhatti | 3 | 59 | 7.81 |
| Sabine Süsstrunk | 4 | 4984 | 207.02 |