Robust periocular biometrics based on Local Phase Quantisation and Gabor transform

In this paper, we propose a novel and robust approach for periocular recognition. Specifically, we propose fusion of Local Phase Quantization(LPQ) and Gabor wavelet descriptors to improve recognition performance and achieve robustness. We have utilized publicly available challenging still face images databases; MBGC v2.0, GTDB, PUT and Caltech. In the approach face is detected and normalized using eye centres. The region around left and right eyes, including eyebrow is extracted as left periocular and right periocular. The LPQ descriptor is then applied to extract the phase statistics features computed locally in a rectangular window. The descriptor is invariant to blur and also to uniform illumination changes. We also computed the Gabor magnitude response of the image, which encodes shape information over a broader range of scales. To reduce dimensionality of the operators and to extract discriminative features, we further utilized DLDA (Direct Linear Discriminant Analysis). The experimental analysis demonstrate that combination of LPQ and Gabor scores provides significant improvement in the performance and robustness, than applied individually.