Abstract
This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP), which is based on Daugman's method for iris recognition and the local XOR pattern (LXP) operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC) method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP) using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.
Highlights
A good object representation or pattern representation is one of the key issues for a well-designed pattern recognition system
In order to reserve the spatial information in the histogram features, local Gabor phase pattern (LGPP) are spatially divided into nonoverlapping rectangular regions represented by R1, . . . , RL, from which local histogram features are extracted, respectively, and all these histograms are concatenated into a single extended histogram feature, the so-called joint local-histogram feature (JLHF), for all frequencies and orientations
Unlike traditional Gabor usage exploitingonly the magnitude information in face recognition, this paper proposes to encode the Gabor phase angle for face classification by quadrant bit coding (QBC)and local XOR pattern (LXP) operator
Summary
A good object representation or pattern representation is one of the key issues for a well-designed pattern recognition system. Our former work, the socalled histogram of Gabor phase pattern (HGPP), encodes the Gabor phase variation derived from orientation change and local phase variations [13] These methods are, in nature, EURASIP Journal on Advances in Signal Processing based on spatial histograms, which can capture the structure information of the input face object and provide an easy matching strategy. Histogram intersection (HI) [14] and Gaussian-weighted chi-squared (GWchi) [15] functions have been proved to be positive definite, which were smoothly used in support vector machine (SVM) classifier [14, 15] They show us that kernel methods can be successfully combined with the histogram feature, and motivate us to make kernel Fisher discriminant analysis for HLGPP (K-HLGPP). Some brief conclusions are drawn with some discussion on the future work
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