Reliable detection of eye features and eyes in color facial images using ternary eye-verifier

Eye detection plays an important role in applications related to face recognition. The position of eyes can be used as a reliable reference for other facial feature detection. This paper presents a novel approach for the precise and reliable detection of eyes by introducing a ternary eye-verifier. Initially, the face region is detected by combining color information and the Haar-like feature detector. The face region is then binarized and filtered with circular filters to detect eye candidates at the peaks in the filtered response. Each eye candidate is fed into a ternary eye-verifier that includes a proposed eye feature extractor based on K-means clustering with compensation for variety in iris color. The eye template in the eye-verifier is constructed based on both the knowledge of eye geometry and the detected eye features. The template matching is made by the ternary Hamming distance. Experiments over a collection of FERET face database and house-made face database with different head poses confirm that the proposed method achieves precise and reliable detection of eyes from color facial images with variation in illumination, pose, eye gazing direction, and race.

► Eye is verified separately without eye pair. ► Eye-verifier considers the eye structure, and eye features. ► Eye feature extraction uses K-means and iris color compensation. ► Proposed method is robust to illumination, head pose, eye gaze direction and race.