Periocular Localization and Feature Extraction for Human Recognition
Supervisor(s) and Committee member(s): Pankaj K. Sa (supervisor), Banshidhar Majhi, Bidyadhar Subudhi, and Sukadev Meher (Advisors)
The advent of biometric system as a next-generation solution towards bringing social and national security to a technically-achievable scenario. This paradigm of authentication has easily taken over the classical token-based and knowledge-based systems. The last decade has seen researches claiming face and iris to be the most promising two traits. Iris produces high accuracy with extremely high-resolution near-infrared (NIR) images, and face is capable of producing moderate accuracy even from low resolution images. To bridge the gap between these two, periocular (periphery of ocular) biometric came into highlight and researchers have initially established its ability to yield accurate recognition.
This thesis attempts to design a periocular biometric system. Periocular region can be considered as the region around eye where features, that can participate in uniquely identifying an individual, are existing. So, starting from eye, while moving away from eye, periocular region ranges up to the portion where the skin becomes smooth and no feature is available. Hence periocular biometric, unlike most common segmentation application, cannot be localized through edge detection. The first part of the thesis investigates to identify four trait-specific localization techniques. For achieving perfect localization, (a) conformation of the localization to human anthropometry, (b) high accuracy from a localized image, (c) conformation to human judgement, and (d) subdivision of eye portion are done. The second part concentrates to design a suitable feature extraction method for periocular biometric. The thesis presents a novel Phase Intensive Global Pattern (PIGP) which is shown to able to extract gross as well as subtle features and work well for images without rotation. The next part of the thesis incorporates and ensures scale-invariant and rotation-invariant properties into PIGP, and this modified version is termed as Phase Intensive Local Pattern (PILP). PILP is experimentally proven to work well for NIR databases as well as visual-spectrum (VS) databases. Ability of PILP to identify large number of potential keypoints and extraction of high-dimensional (128D) feature from them results into the high accurate performance of PILP. However, this type of phase-difference based keypoint detection and oriented histogram based large feature extraction is extremely time-consuming and the feature vector, being so large, invites a reduction technique to be employed. The next part of the thesis hence develops a post-reduction technique to reduce the feature vector size and thereby the matching time. Reduced PILP (R-PILP) is developed from PILP by classifying keypoints through verifying the degree of monotonic nature in them. Experiments show that R-PILP is a little less accurate than PILP but R-PILP is faster as compared.
All results in the thesis have been derived on four standard publicly available databases: BATH and CASIAv3 (NIR databases), and UBIRISv2 and FERETv4 (VS databases). Comparative analysis have been made with existing landmark techniques like Circular Local Binary Pattern (CLBP), Walsh Mask, Scale Invariant Feature Transform (SIFT), and Speeded Up Robust Features (SURF). It has been observed that these features consistently
work equally well on NIR databases. However, performance of existing techniques degrade rapidly when experimented on VS databases. Though the proposed techniques suffers degradation, but outperforms the existing techniques with a high margin. The localization technique, and three progressively developed features PIGP, PILP, and R-PILP complete the objective of developing the periocular biometric system.
Intelligent Computing and Computer Vision Group
Headed by Prof. Banshidhar Majhi, Intelligent Computing and Computer Vision Group in Department of Computer Science & Engineering of National Institute of Technology Rourkela strives to research in the challenging field of Visual Surveillance, Biomedical Imaging, Biometrics, Robotic Vision, Action, Event, and Emotion Recognition, Video Summarization, Person Tracking and Reidentification, Scene Perception, Semantic Scene Segmentation etc. With the promising results in the domain, the group has also come up with a Centre of Research in the institute named Centre for Computer Vision & Pattern Recognition. The investigators of the center will further move ahead the vision of the group and concentrate on researches targeting to cater the need of human society.