An Improved Algorithm for Eye Corner Detection

TL;DR

This paper presents an improved eye corner detection algorithm that combines face and eye detection, sclera segmentation, eyelid contour approximation, and Harris corner detection with post-pruning, tested on multiple databases.

Contribution

It introduces a modified algorithm based on the Santos and Proenka method, enhancing eye corner detection accuracy through segmentation and candidate pruning.

Findings

Effective on Yale, JAFFE, and custom databases.

Improved accuracy over previous methods.

Robust detection of nasal and temporal eye corners.

Abstract

In this paper, a modified algorithm for the detection of nasal and temporal eye corners is presented. The algorithm is a modification of the Santos and Proenka Method. In the first step, we detect the face and the eyes using classifiers based on Haar-like features. We then segment out the sclera, from the detected eye region. From the segmented sclera, we segment out an approximate eyelid contour. Eye corner candidates are obtained using Harris and Stephens corner detector. We introduce a post-pruning of the Eye corner candidates to locate the eye corners, finally. The algorithm has been tested on Yale, JAFFE databases as well as our created database.