Scale-level Score Fusion of Steered Pyramid Features for Cross-Spectral Periocular Verification

Periocular characteristics has gained substantial importance in recent times to supplement the performance of facial biometrics or as a stand-alone characteristics. While most of the current biometric systems for authentication or surveillance operate either in NIR spectrum or visible spectrum, the ocular information can be well utilized if a comparison of images from different spectra has to be conducted. In this work, we present a novel approach employing the features obtained from steerable pyramids to compare the ocular images captured from NIR versus the images captured from visible spectrum. The set of features obtained using the proposed cross-spectral approach are then used to learn a multi-class SVM classifier such that the probe image originating from another spectrum can be classified. Further, a fusion frame-work for combining the scores from different orientations of the steerable pyramid is proposed for a particular scale to strengthen the biometric performance of the algorithm. An extensive set of experiments conducted on a large database consisting of ocular images captured from 120 subjects (240 unique ocular instances) indicates the robustness of the proposed approach with a GMR of 100% at the FMR of 0.01% in a benchmark against other state-of-the-art techniques suggesting the applicability of proposed approach to greater extent.