Robust local binary pattern feature sets for periocular biometric identification
In this paper, we perform a detailed investigation of various features that can be extracted from the periocular region of human faces for biometric identification. The emphasis of this study is to explore the BEST feature extraction approach used in stand-alone mode without any generative or discriminative subspace training. Simple distance measures are used to determine the verification rate (VR) on a very large dataset. Several filter-based techniques and local feature extraction methods are explored in this study, where we show an increase of 15% verification performance at 0.1% false accept rate (FAR) compared to raw pixels with the proposed Local Walsh-Transform Binary Pattern encoding. Additionally, when fusing our best feature extraction method with Kernel Correlation Feature Analysis (KCFA) [36], we were able to obtain VR of 61.2%. Our experiments are carried out on the large validation set of the NIST FRGC database [6], which contains facial images from environments with uncontrolled illumination. Verification experiments based on a pure 1–1 similarity matrix of 16028×8014 (~128 million comparisons) carried out on the entire database, where we find that we can achieve a raw VR of 17.0% at 0.1% FAR using our proposed Local Walsh-Transform Binary Pattern approach. This result, while may seem low, is more than the NIST reported baseline VR on the same dataset (12% at 0.1% FAR), when PCA was trained on the entire facial features for recognition [6].