Ocular Recognition for Blinking Eyes

Abstract

Ocular recognition is expected to provide a higher flexibility in handling practical applications as oppose to the iris recognition, which only works for the ideal open-eye case. However, the accuracy of the recent efforts is still far from satisfactory at uncontrollable conditions, such as eye blinking which implies any poses of eyes. To address these issues, the skin texture, eyelids, and additional geometrical features are employed. In addition, to achieve higher accuracy, sequential forward floating selection (SFFS)is utilized to select the best feature combinations. Finally, the non-linear SVM is applied for identification purpose.

Existing System

Multiple Classifier Techniques.

Proposed System

This paper proposes an ocular recognition of iris recognition. In the proposed algorithm, the landmark points of eyes are firstly detected. To suppress the influences of skin colors and lighting conditions, the geometric feature, which particularly describes the contours of eyes, are considered. The weighted texture-based LBP (WT-LBP) is proposed to extract the texture property of the selected regions. In addition, the probabilities of single-and double-fold eyelids are also derived for description. To further enhance the performance, the combination of various features with the sequential forward floating selection (SFFS) is further utilized. Finally, the non-linear support vector machine (SVM) is applied for classification purpose.

Architecture

BLOCK DIAGRAM