A Real Time Robust Eye Center Localization using Geometric Eye Model and Edge Gradients in Unconstrained Visual Environment

Accurate eye localization is an essential and fundamental step in initialization for other eye tracking applications. A fast and accurate eye center localization method is proposed in this paper. A novel geometric eye model is derived based on face anthropometry parameters to identify coarse eye region correctly. A novel voting method using edge gradients on the iris boundary are used for fast and accurate eye center localization. A precise range around the boundary of an iris is derived to indicate the region within which the gradients are allowed to vote. Additionally the range between the pupil centers is derived to validate the search region. A weight map is generated for efficient computation, which is combined with edge gradients and the maximum of the multiplication between the dot products and the weight from the map is identified as the eye center. The proposed method is evaluated on challenging BioID database and found to be highly accurate for eye center localization task. The proposed method is efficient under natural lighting condition, has low computational complexity and excellent real-time ability. Keywords Eye center localization, Gaze Estimation, Human Computer Interaction. 1. INTRODUCTION Eye localization is fundamental to all eye tracking applications such as eye gaze estimation in Human Computer Interaction(HCI), human cognitive analysis, website and product usability testing, educational training, driver assistance system etc. The task of eye localization is difficult due to variations in appearance, facial expression, occlusion, pose, illumination, imaging quality and motion characteristics of an eye and may lead to a complex system design. There are many intrusive techniques available for eye center localization that makes use of electrooculography, Scleral coils, head mounted device with active infrared illumination. These techniques permit very accurate eye center localization however they have serious health concerns and are less efficient in daylight condition and outdoor situations. A vision based method is more appropriate in these situations however it is less accurate due to variations and appearance of an eye characteristic in real time scenarios.Most video based eye localization methods adopt two-stage analysis as face detection and eye localization, where the face is located first and the eyes are then detected by searching them within the possible eye region in the face. Correct face detection would reduce the search space and increase the accuracy of eye detection. There are various face and eye detection methods broadly classified as following: 1.1 Face detection methods A. Template matching methods : Here, the templates of various facial feature are constructed which are then compared with an input image. The eigenface [1, 2] is widely used template matching method where the eigenface of the input face image is compared with all the templates in the database to estimate the corresponding similarities and most matching one is decided by the similarity values. However, these methods are inefficient in case of affine transformation such as scaling, rotation, skew etc. B. Appearance-based methods : These methods consider features of the whole face such as skin colour, face contour, facial feature etc. as a single feature does not provide enough information for the face detection. They also use various machine learning techniques to learn these features to classify the face patterns. Viola Jones[3] method based on integral image and Ada Boost cascade classifier performs well for face detection in most of the cases. 1.2 Eye detection methods A. Shape based methods : The eyes can be described by their shape, which is composed of an iris, pupil, and the eyelids. The ellipse shape of an eye or circular shape of an iris is a frequently used feature in shape based methods. However these methods require high contras images and may not work for dark iris. Another variation is to construct a deformable model [4] and use a template to identify the corresponding feature points of the eye, but these approaches are computationally complex. B. Appearance-based methods : These methods detect eyes based on photometric appearance, including colour information and the surroundings of an eye such as template matching[5] or pattern matching based on Haar Like features[6,7]. These methods need large learning samples to provide accuracy to various deformations. These methods deal with rotations and scaling but fails if the hair or the glasses occlude the eyes. 2. RELATED WORK Jesorsky[8] were among the first to highlight the performance and accuracy of the face detection. He proposed a measure for quantitative evaluation for the performance of Eye Localization methods thru Hausdorff distance (HD), which is widely used nowadays. The Hausdorff distance (HD) is a metric between two point sets that measure a relative error based on the distances between the expected International Journal of Computer Applications (0975 – 8887) Volume 128 – No.1, October 2015 23 and the estimated eye positions to validate the performance of the eye detection system. Recently, there have been many leading methods available for eye localization. They are based on Pairwise Reinforcement of Feature Responses (PRFR) and an Active Appearance Model (AAM) [11], Registration and Appearance Verification[12], 2D cascade classifier with Large training dataset[13], edge projection using wavelets[14], Isophote Curvature with MIC+SIFT+kNN Learning [15], cumulative density function (CDF) framework[16], Multiscale Sparse Dictionaries (MSD)[17], image gradients[18], E-LBP with probabilistic cascade (PCascade) framework[19], Scale-invariant feature transform (SIFT) and 2-class sparse representation classifier (SRC)[20]. The in depth review and performance analysis of state-of-the art methods, based on their existing published results that adopt similar evaluation criteria for objective performance evaluation is available at [9][10]. 3. PROBLEM STATEMENT In uncontrolled scenarios, the geometrical structures and the appearance of an eye is drastically different from the predefined template or training data set and the accuracy of most of the eye localization methods decrease significantly. Therefore, in spite of an extensive research, the reliable eye localization in uncontrolled environment still needs to be addressed thoroughly. The performance analysis of various methods based on Normalized Error Measurement for various error value of e Î{0.05, 0.10, 0.15, 0.20, 0.25} is carried out at [10]. This measure indicate that for e ≤ 0.05 the eye center is within diameter of the pupil, for e ≤ 0.10, it is within diameter of the iris and for e ≤ 0.25, it is within the eye center and the eye corners. For eye center localization task, a good performance of e≤0.05 is highly essential. However, higher values (e≤0.25) are acceptable for other general applications such as eye detection, face detection, facial feature extraction etc. The methods[17][19][20] that claim to have higher accuracy (e £0.05) are complex, require some machine learning techniques and computationally expensive. The method[16][18] that claim to be computationally efficient is not highly accurate (for e≤0.05), which is the prime requirement for efficient eye center localization system. Therefor the aim is to propose a visible spectrum eye center localization method that is accurate (for e £0.05) as well as computationally efficient for real time eye center localization in uncontrolled scenarios. 4. OVERVIEW OF PROPOSED APPROACH A stage wise approach is adopted to perform the eye localization task. The face detection is achieved by Viola Jones boosted cascade classifier[3] in first stage, which performs well in real time to detect faces. The coarse eye regions are derived from the detected face to perform further analysis in second stage. Since the Pixels on the boundary of the iris and pupil have the same intensity, the outlines of iris and pupil can be simplified to circles. The vector field of an image gradient can appropriately be used to detect the center of the circular region. The gradient-based objective function[18] estimates the iris center by searching the maximum value of the function as the iris center. However this method makes redundant searches in the larger eye region and fails to detect eye center in case of prominent eyebrows and eyes obstructed by hair or glasses. A more efficient approach is proposed here, where a precise coarse eye region is identified using novel geometric eye model derived using face anthropometric parameters, which is further used for eye center localization. Novel range of gradients around the boundary of an iris is proposed that can more precisely contribute to vote for the accurate eye center localization. This approach is faster and eliminates redundant searches. These gradients are then combined with a weight map to detect iris center in an effective and accurate way. 5. PROPOSED GEOMETRIC EYE MODEL AND EDGE GRADIENTS Face anthropometry parameters are used to identify certain landmark points to detect facial features from the face region. A novel geometric eye model based on these parameters is proposed to identify coarse eye region related to the size of detected faces. Precise eye localization can then be achieved from the coarse eye region derived according to this parameter. 5.1 Coarse Eye Region According to prendergast[22], the eyes usually measures one-fifth the width and one third the height of the face. This is shown in figure 1 using BioID database. Here w and H are the width and height of the face region detected by voila Jones[3]. Figure 1 : Face anthropometric measures on BioID database. Figure 2 shows the proposed geometric eye model to improve the coarse eye region detection. Here α is the width of the coarse eye region, which is approximately 1/5 of the relative face width w, r is the radius of the iris, which is rou