A Robust Algorithm for Eye Detection on Gray Intensity Face without Spectacles
Kun Peng, Liming Chen, LIRIS, Département MI, Ecole Centrale de Lyon 36 avenue Guy de Collongue, BP 163, 69131 Ecully, France Su Ruan, Equipe Image, CReSTIC, Département GE&II, IUT de Troyes 9 rue de Quebec, 10026 Troyes, France and Georgy Kukharev Faculty of Computer Science andInformation Technology, Technical University of Szczecin Zolnierska 49, 71-210 Szczecin, Poland firstname.lastname@example.org ABSTRACT
This paper presents a robust eye detection algorithm for gray intensity images. The idea of our method is to combine the respective advantages of two existing techniques, feature based method and template based method, and to overcome their shortcomings. Firstly, after thelocation of face region is detected, a feature based method will be used to detect two rough regions of both eyes on the face. Then an accurate detection of iris centers will be continued by applying a template based method in these two rough regions. Results of experiments to the faces without spectacles show that the proposed approach is not only robust but also quite efficient. Keywords: Eyedetection, Face detection, Face recognition, Image processing, Pattern recognition second plays an important role in applications such as video conferencing and vision assisted user interface ). However, most algorithms for eye contour detection, which use the deformable template proposed by Yuille et al. , require the detection of eye positions to initialize eye templates. Thus, eye positiondetection is important not only for face recognition and facial expression analysis but also for eye contour detection. In this paper eye detection means eye position detection.
The existing work in eye position detection can be classified into two categories: active infrared (IR) based approaches and image-based passive approaches. Eye detection based on active remote IRillumination is a simple yet effective approach. But they all rely on an active IR light source to produce the dark or bright pupil effects. In other words, these methods can only be applied to the IR illuminated eye images. It’s certain that these methods would not be widely used, because in many real applications the face images are not IR illuminated. Thus this paper only focuses on the image-basedpassive methods, which can be broadly classified into three categories: template based methods [3-6], appearance based methods [7-9] and feature based methods [10-14]. In the template based methods, a generic eye model, based on the eye shape, is designed firstly. Template matching is then used to search the image for the eyes. While these methods can detect eyes accurately, they are normallytime-consuming. The appearance based methods [7-9] detect eyes
As one of the salient features of the human face, human eyes play an important role in face recognition and facial expression analysis. In fact, the eyes can be considered salient and relatively stable feature on the face in comparison with other facial features. Therefore, when we detect facial features, it isadvantageous to detect eyes before the detection of other facial features. The position of other facial features can be estimated using the eye position . In addition, the size, the location and the image-plane rotation of face in the image can be normalized by only the position of both eyes. Eye detection is divided into eye position detection [1, 2] and eye contour detection [3, 15, 16]. (The127
JCS&T Vol. 5 No. 3
based on their photometric appearance. These methods usually need to collect a large amount of training data, representing the eyes of different subjects, under different face orientations, and under different illumination conditions. These data are used to train a classifier such as a neural network or the support vector machine and detection is...