Name
Abstract | ||
|---|---|---|
This paper presents a simple, novel, yet highly effective approach for robust face recognition. Given LBP-like descriptors based on local accumulated pixel differences, Angular Differences (AD) and Radial Differences (RD), the local differences are decomposed into complementary components of signs and magnitudes. The proposed descriptors have desirable features: (1) robustness to lighting, pose, and expression; (2) computation efficiency; (3) encoding of both microstructures and macrostructures; (4) consistent in form with traditional LBP, thus inheriting the merits of LBP; and (5) no required training, improving generalizability. From a given face image, we obtain six histogram features, each of which is obtained by concatenating spatial histograms extracted from nonoverlapping subregions. The Whitened PCA technique is used for dimensionality reduction, followed by Nearest Neighbor classification. We have evaluated the effectiveness of the proposed method on the Extended Yale B and CAS-PEAL-R1 databases. The proposed method impressively outperforms other well known systems, including what we believe to be the best reported performance for the the CAS-PEAL-R1 lighting probe set with a recognition rate of 72.3%. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICIP.2014.7025144 | ICIP |
Keywords | Field | DocType |
nonoverlapping subregion extraction,image fusion,face recognition,feature extraction,spatial histogram feature extraction,angular differences,local binary pattern,macrostructures,cas-peal-r1 databases,ad,pose features,radial differences,local descriptors,vocabulary,microstructures,extended local binary pattern fusion,dimensionality reduction,encoding,rd,whitened pca technique,nearest neighbor classification,face image recognition,local accumulated pixel differences,signs and magnitude complementary components,generalizability improvement,lbp-like descriptors,binary codes,principal component analysis,expression features,extended yale b databases,computation efficiency,lighting probe set | k-nearest neighbors algorithm,Facial recognition system,Computer vision,Histogram,Dimensionality reduction,Pattern recognition,Computer science,Local binary patterns,Robustness (computer science),Feature extraction,Artificial intelligence,Pixel | Conference |
ISSN | Citations | PageRank |
1522-4880 | 0 | 0.34 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Liu | 1 | 733 | 50.04 |
| Paul W. Fieguth | 2 | 612 | 54.17 |
| Guoying Zhao | 3 | 3767 | 166.92 |
| Matti Pietikäinen | 4 | 14779 | 739.80 |