A multistage procedure to detect eye features is presented. Multiresolution and topographic classification are used to detect the iris center. The eye corner is calculated combining valley detection and eyelid curve extraction. The algorithm is tested in the BioID database and in a proprietary database containing more than 1200 images. The results show that the suggested algorithm is robust and accurate. Regarding the iris center our method obtains the best average behavior for the BioID database compared to other available algorithms. Additional contributions are that our algorithm functions in real time and does not require complex post processing stages.

/pdf/hybrid-method-based-on-topography-for-robust-detection-of-2k74nsm06o.pdf

Hybrid method based on topography for robust detection of iris center and eye corners

Accurate detection of iris center and eye corners appears to be a promising approach for low cost gaze estimation. In this paper we propose novel eye inner corner detection methods. Appearance and feature based segmentation approaches are suggested. All these methods are exhaustively tested on a realistic dataset containing images of subjects gazing at different points on a screen. We have demonstrated that a method based on a neural network presents the best performance even in light changing scenarios. In addition to this method, algorithms based on AAM and Harris corner detector present better accuracies than recent high performance face points tracking methods such as Intraface.

/pdf/evaluation-of-accurate-eye-corner-detection-methods-for-gaze-1v6yuub85t.pdf

Evaluation of accurate eye corner detection methods for gaze estimation

Corner detection has motivated a great deal of research and is particularly important in a variety of tasks related to computer vision, acting as a basis for further stages. In particular, the detection of eye-corners in facial images is important in applications in biometric systems and assisted-driving systems. We empirically evaluated the state-of-the-art of eye-corner detection proposals and found that they achieve satisfactory results only when dealing with high-quality data. Hence, in this paper, we describe an eye-corner detection method that emphasizes robustness, i.e., its ability to deal with degraded data, and applicability to real-world conditions. Our experiments show that the proposed method outperforms others in both noise-free and degraded data (blurred and rotated images and images with significant variations in scale), which is a major achievement.

A robust eye-corner detection method for real-world data

Eye features are one of the most important clues for many computer vision applications. In this paper, an efficient method to automatically extract eye features is presented. The extraction is highly based on the usage of the common knowledge about face and eye structure. With the assumption of frontal face images, firstly coarse eye regions are extracted by removing skin pixels in the upper part of the face. Then, iris circle position and radius are detected by using Hough transform in a coarse-to-fine fashion. In the final step, edges created by upper and lower eyelids are detected and polynomials are fitted to those edges so that their intersection points are labeled as eye corners. The algorithm is experimented on the Bosphorus database and the obtained results demonstrate that it can locate eye features very accurately. The strength of the proposed method stems from its reproducibility due to the utilization of simple and efficient image processing methods while achieving remarkable results without any need of training.

/pdf/an-efficient-iris-and-eye-corners-extraction-method-45u6afw9cm.pdf

An efficient iris and eye corners extraction method

Eye gaze detection under challenging lighting conditions is a non-trivial task. Pixel intensity and the shades around the eye region may change depending on the time of day, location, or due to artificial lighting. This paper introduces a lighting-adaptive solution for robust eye gaze detection. First, we propose a binarization and cropping technique to limit our region of interest. Then we develop a gradient-based method for eye-pupil detection; and finally, we introduce an adaptive eye-corner detection technique that altogether lead to robust eye gaze estimation. Experimental results show the outperformance of the proposed method compared with related techniques.

/pdf/robust-eye-gaze-estimation-4uzyp745nm.pdf

Robust Eye Gaze Estimation

A novel eye corner detection method is presented in this paper. The method is capable of detecting a pair of inner and outer eye corners from a complex background. Faces and eyes are first detected from the image by Adaboost with Haar-like features. Next, the potential area of the eye corner is determined based on variance projection function. In view of the corner characteristics of the eye corner, the weighted variance projection function is proposed to locate the eye corner within the potential area, which combines Harris's response function with variance projection function. The robustness and accuracy of the proposed method are demonstrated in experiment.

Haiying Xia

Papers

A Novel Method for Eye Corner Detection Based on Weighted Variance Projection Function