Local matching Gabor entropy weighted face recognition

Abstract

Face recognition has a wide range of possible applications in surveillance, access control, human computer interfaces and in electronic marketing and advertising for selected customers. Several models based on Gabor feature extraction have been proposed for face recognition with very good results on internationally available face databases. In this paper, we propose a methodological improvement to increase face recognition rate by selection and weighting Gabor jets by an entropy measure. We also propose improvements in the Borda count classification through a threshold to eliminate low score jets from the voting process to increase the face recognition rate. We show that combinations of weighting Gabor jets and threshold Borda yield the best results. We tested our methodological improvements on the FERET and the AR face databases. On the FERET database we reduce the total number of errors from 163 to 102 which is the highest score published up to date. The total number of errors in face recognition was reduced in 37%. On the AR database we also obtained important improvements and tested face images with illumination and gesticulation changes, and occlusions.

Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models

“…In our previous work based on Gabor-feature face classification [26], faces are normalized using the eye position for coronal axis rotations (on the same face plane). Also, our previously proposed method included weights for the Gabor jets using an entropy measure and a preprocessing step with Local Normalization (LN) yielding results that are among the best face classification results reported on the FERET and AR databases [25,26,47]. Our method performed competitively with other published methods on face occlusions and in the presence of noise.…”

“…A modified ranking matrix, Q ln j,i , is created by (9), where T h is the threshold that eliminates noisy scores. This noisy score elimination has yielded significant improvements in classification results [25,26].…”

“…Based on our previous work [25,26] the threshold was set to T h = 0.85. The identification scores S j are obtained using Q ln instead of O (Eq.…”

“…Several recent articles have shown that the Local Matching Gabor (LMG) method [24] and its variants [25,26] reach the best results for frontal face recognition. In the LMG method, a total of 4,172 Gabor jets are employed to extract features at five different spatial resolutions and eight different orientations.…”

“…Among the methods based on Gabor Wavelets are the Elastic Bunch Graph Matching (EBGM) method [17], the Gabor Fisher Classifier (GFC) [18], the Local Gabor Binary Pattern Histogram Sequence (LGBPHS) [19], the Histogram of Gabor Phase Patterns (HGPP) [20], Local Gabor Textons (LGT) [21], Learned Local Gabor Pattern (LLGP) [22], Local Gabor Binary Pattern Whitening PCA (LGBPWP) [23], and the Local Matching Gabor method (LMG) [24][25][26][27]. In [28], the face was divided into patches without overlap, and then the best patches were selected and weighted with an LDA strategy in a greedy search.…”