Investigation of rotated local Gabor features in face recognition using fusion techniques

One of the major challenges related to face recognition is the effective face representation. In this paper, a new rotation algorithm is proposed to make the slanting face to upright position. Gabor features have been identified to be effective for face recognition. To investigate the prospective of Gabor phase and its combination with magnitude for face recognition, Rotated Local Gabor XOR patterns (RLGXP), which encodes the Rotated Gabor phase by using the local XOR pattern (LXP) operator is first proposed, which increases the effectiveness of face representation. Then, Cell-based Fishers linear discriminant (CFLD) to reduce the dimensionality of the proposed descriptor is introduced. Finally, by using CFLD, local patterns of Rotated Gabor magnitude and phase for face recognition are combined. This proposed approach is evaluated on MIT, GTAVE, PIE, CMU and Home databases. Moreover, to enhance the effectiveness of face representation through feature design (RLGXP), the Gabor filter parameters like mask size, scale and orientation are very important. The effectiveness of the rotating local Gabor model + CFLD method was verified for upslopes and uprights of different data sets. The analysis of face feature representation using the RLGXP + CFLD method basically depends on the phase range, the appropriate choice of the number of sub-cells per pattern map and size of neasret region. Here, the sensitivity analysis used for the above three parameters is best selected and executed. This paper also investigates the effect of different Gabor filter parameters on face recognition accuracy. Parameter Selection Investigation result proves that Gabor wavelets comprising 5 scales and 8 orientations have been chosen to extract Facial Feature Points (FFP). The final result shows that Rotated Local Gabor Features provides the best face recognition accuracy.

Automated summary

This paper proposes a new approach for face recognition by enhancing face representation effectiveness through rotation algorithm and Gabor features, along with adding Cell-based Fishers linear discriminant to reduce dimensionality. The study investigates the impact of different Gabor filter parameters on face recognition accuracy, concluding that Rotated Local Gabor Features provide the best face recognition accuracy.