Name
Title | ||
|---|---|---|
Enhanced retinal modeling for face recognition and facial feature point detection under complex illumination conditions |
Abstract | ||
|---|---|---|
We improved classic retinal modeling to alleviate the adverse effect of complex illumination on face recognition and extracted robust image features. Our improvements on classic retinal modeling included three aspects. First, a combined filtering scheme was applied to simulate functions of horizontal and amacrine cells for accurate local illumination estimation. Second, we developed an optimal threshold method for illumination classification. Finally, we proposed an adaptive factor acquisition model based on the arctangent function. Experimental results on the combined Yale B; the Carnegie Mellon University poses, illumination, and expression; and the Labeled Face Parts in the Wild databases show that the proposed method can effectively alleviate illumination difference of images under complex illumination conditions, which is helpful for improving the accuracy of face recognition and that of facial feature point detection. (C) 2016 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1117/1.JEI.25.4.043028 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
local illumination estimation,adaptive factor,illumination nonlinear adaptation,face recognition,facial feature point detection | Facial recognition system,Computer vision,Pattern recognition,Three-dimensional face recognition,Computer science,Feature (computer vision),Filter (signal processing),Artificial intelligence,Retinal,Local illumination,Inverse trigonometric functions | Journal |
Volume | Issue | ISSN |
25 | 4 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Cheng | 1 | 21 | 5.17 |
| Yong Cheng | 2 | 21 | 5.17 |
| Zuoyong Li | 3 | 348 | 27.55 |
| Liangbao Jiao | 4 | 12 | 2.99 |
| Hong Lu | 5 | 2 | 2.06 |
| Xuehong Cao | 6 | 8 | 2.19 |