A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Two new algorithms for computer recognition of human faces, one based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second based on almost-gray-level template matching, are presented. The results obtained for the testing sets show about 90% correct recognition using geometrical features and perfect recognition using template matching. > <s> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> In this paper we investigate the performance of a technique for face recognition based on the computation of 25 local autocorrelation coefficients. We use a large database of 11,600 frontal facial images of 116 persons, organized in training and test sets, for evaluation. Autocorrelation coefficients are computationally inexpensive, inherently shift-invariant and quite robust against changes in facial expression. We focus on the difficult problem of recognizing a large number of known human faces while rejecting other, unknown faces which lie quite close in pattern space. A multiresolution system achieves a recognition rate of 95%, while falsely accepting only 1.5% of unknown faces. It operates at a speed of about one face per second. Without rejection of unknown faces, we obtain a peak recognition rate of 99.9%. The good performance indicates that local autocorrelation coefficients have a surprisingly high information content. <s> BIB002 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Direct observation and recorded color images of the same scenes are often strikingly different because human visual perception computes the conscious representation with vivid color and detail in shadows, and with resistance to spectral shifts in the scene illuminant. A computation for color images that approaches fidelity to scene observation must combine dynamic range compression, color consistency-a computational analog for human vision color constancy-and color and lightness tonal rendition. In this paper, we extend a previously designed single-scale center/surround retinex to a multiscale version that achieves simultaneous dynamic range compression/color consistency/lightness rendition. This extension fails to produce good color rendition for a class of images that contain violations of the gray-world assumption implicit to the theoretical foundation of the retinex. Therefore, we define a method of color restoration that corrects for this deficiency at the cost of a modest dilution in color consistency. Extensive testing of the multiscale retinex with color restoration on several test scenes and over a hundred images did not reveal any pathological behaviour. <s> BIB003 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> A face recognition system must recognize a face from a novel image despite the variations between images of the same face. A common approach to overcoming image variations because of changes in the illumination conditions is to use image representations that are relatively insensitive to these variations. Examples of such representations are edge maps, image intensity derivatives, and images convolved with 2D Gabor-like filters. Here we present an empirical study that evaluates the sensitivity of these representations to changes in illumination, as well as viewpoint and facial expression. Our findings indicated that none of the representations considered is sufficient by itself to overcome image variations because of a change in the direction of illumination. Similar results were obtained for changes due to viewpoint and expression. Image representations that emphasized the horizontal features were found to be less sensitive to changes in the direction of illumination. However, systems based only on such representations failed to recognize up to 20 percent of the faces in our database. Humans performed considerably better under the same conditions. We discuss possible reasons for this superiority and alternative methods for overcoming illumination effects in recognition. <s> BIB004 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> We present a system for recognizing human faces from single images out of a large database with one image per person. The task is difficult because of image variation in terms of position, size, expression, and pose. The system collapses most of this variance by extracting concise face descriptions in the form of image graphs. In these, fiducial points on the face (eyes, mouth etc.) are described by sets of wavelet components (jets). Image graph extraction is based on a novel approach, the bunch graph, which is constructed from a small set of sample image graphs. Recognition is based on a straight-forward comparison of image graphs. We report recognition experiments on the FERET database and the Bochum database, including recognition across pose. <s> BIB005 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> A novel algorithm for front-view facial contour detection and features extraction is described. A skin-color face segmentation method is developed to detect the face region firstly. In order to more precisely locate the face contour and features such as eyes, mouth and nostrils, we eliminate the ears and neck from the face region by using morphological operations and knowledge about the face structure. We have found some consistent rules that can be used to locate some contour points from the skeleton of the processed frontal face region. The face contour can then be fitted as an ellipse using these points. Then the facial features can be located in the interior of the face contour. Experiments have been done with a number of images of frontal view of the head including some with a slight pan obtained from an Internet face database (University of Stirling). Also a number of face images captured by a color CCD camera are tried by our method. The robustness is confirmed with correct detection rate of over 90%. <s> BIB006 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Human face detection is often the first step in applications such as video surveillance, human computer interface, face recognition, and image database management. We propose a face detection algorithm for color images in the presence of varying lighting conditions as well as complex backgrounds. Our method detects skin regions over the entire image, and then generates face candidates based on the spatial arrangement of these skin patches. The algorithm constructs eye, mouth, and boundary maps for verifying each face candidate. Experimental results demonstrate successful detection over a wide variety of facial variations in color, position, scale, rotation, pose, and expression from several photo collections. <s> BIB007 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Face recognition algorithms have to deal with significant amounts of illumination variations between gallery and probe images. State-of-the-art commercial face recognition algorithms still struggle with this problem. We propose a new image preprocessing algorithm that compensates for illumination variations in images. From a single brightness image the algorithm first estimates the illumination field and then compensates for it to mostly recover the scene reflectance. Unlike previously proposed approaches for illumination compensation, our algorithm does not require any training steps, knowledge of 3D face models or reflective surface models. We apply the algorithm to face images prior to recognition. We demonstrate large performance improvements with several standard face recognition algorithms across multiple, publicly available face databases. <s> BIB008 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> The appearance of an object depends on both the viewpoint from which it is observed and the light sources by which it is illuminated. If the appearance of two objects is never identical for any pose or lighting conditions, then–in theory–the objects can always be distinguished or recognized. The question arises: What is the set of images of an object under all lighting conditions and pose? In this paper, we consider only the set of images of an object under variable illumination, including multiple, extended light sources and shadows. We prove that the set of n-pixel images of a convex object with a Lambertian reflectance function, illuminated by an arbitrary number of point light sources at infinity, forms a convex polyhedral cone in R^n and that the dimension of this illumination cone equals the number of distinct surface normals. Furthermore, the illumination cone can be constructed from as few as three images. In addition, the set of n-pixel images of an object of any shape and with a more general reflectance function, seen under all possible illumination conditions, still forms a convex cone in R^n. Extensions of these results to color images are presented. These results immediately suggest certain approaches to object recognition. Throughout, we present results demonstrating the illumination cone representation. <s> BIB009 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> The eyes are the most important facial landmarks on the human face. The accuracy of face normalization, which is critical to the performance of the following face analysis steps, depends on the locations of the two eyes, as well as their relatively constant interocular distance. In this paper, we propose a novel GaborEye model for eye localization. Based on the special gray distribution in the eye-and-brow region, a proper Gabor kernel is adaptively chosen to convolute with the face image to highlight the eye-and-brow region, which can be exploited to segment the two pupil regions efficiently. After getting the region of the pupil, a fast radial symmetry operator is used to locate the center of the pupil. Extensive experiments show that the method can accurately locate the pupils, and it is robust to the variations of face poses, expressions, accessories and illuminations. <s> BIB010 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> This paper addresses the problem of automatic detection of salient facial features. Face images are described using local normalized Gaussian receptive fields. Face features are learned using a clustering of the Gaussian derivative responses. We have found that a single cluster provides a robust detector for salient facial features robust to pose, illumination and identity. In this paper we describe how this cluster is learned and which facial features have found to be salient <s> BIB011 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Eye feature location is an important step in automatic visual interpretation and human face recognition. In this paper, a novel approach for locating eye centers in face areas under probabilistic framework is devised. After grossly locating a face, we first find the areas which left and right eyes lies in. Then an appearance-based eye detector is used to detect the possible left and right eye separately. According to their probabilities, the candidates are subsampled to merge those in near positions. Finally, the remaining left and right eye candidates are paired; each possible eye pair is normalized and verified. According to their probabilities, the precise eye positions are decided. The experimental results demonstrate that our method can effectively cope with different eye variations and achieve better location performance on diverse test sets than some newly proposed methods. And the influence of precision of eye location on face recognition is also probed. The location of other face organs such as mouth and nose can be incorporated in the framework easily. <s> BIB012 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Abstract We describe a method for the automatic identification of facial features (eyes, nose, mouth and chin) and the precise localization of their fiducial points (e.g. nose tip, mouth and eye corners) in color images of face foregrounds. The algorithm requires as input 2D color images, representing face foregrounds with homogeneous background; it is scale-independent, it deals with either frontal, rotated (up to 30°) or slightly tilted (up to 10°) faces, and it is robust to different facial expressions, requiring the mouth closed and the eyes opened, and no wearing glasses. The method proceeds with subsequent refinements: first, it identifies the sub images containing each feature, afterwards, it processes the single features separately by a blend of techniques which use both color and shape information. The system has been tested on three databases: the XM2VTS database, the University of Stirling database, and ours, for a total of 1650 images. The obtained results are described quantitatively and discussed. <s> BIB013 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Previous work has demonstrated that the image variation of many objects (human faces in particular) under variable lighting can be effectively modeled by low-dimensional linear spaces, even when there are multiple light sources and shadowing. Basis images spanning this space are usually obtained in one of three ways: a large set of images of the object under different lighting conditions is acquired, and principal component analysis (PCA) is used to estimate a subspace. Alternatively, synthetic images are rendered from a 3D model (perhaps reconstructed from images) under point sources and, again, PCA is used to estimate a subspace. Finally, images rendered from a 3D model under diffuse lighting based on spherical harmonics are directly used as basis images. In this paper, we show how to arrange physical lighting so that the acquired images of each object can be directly used as the basis vectors of a low-dimensional linear space and that this subspace is close to those acquired by the other methods. More specifically, there exist configurations of k point light source directions, with k typically ranging from 5 to 9, such that, by taking k images of an object under these single sources, the resulting subspace is an effective representation for recognition under a wide range of lighting conditions. Since the subspace is generated directly from real images, potentially complex and/or brittle intermediate steps such as 3D reconstruction can be completely avoided; nor is it necessary to acquire large numbers of training images or to physically construct complex diffuse (harmonic) light fields. We validate the use of subspaces constructed in this fashion within the context of face recognition. <s> BIB014 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Many recognition algorithms depend on careful positioning of an object into a canonical pose, so the position of features relative to a fixed coordinate system can be examined. Currently, this positioning is done either manually or by training a class-specialized learning algorithm with samples of the class that have been hand-labeled with parts or poses. In this paper, we describe a novel method to achieve this positioning using poorly aligned examples of a class with no additional labeling. Given a set of unaligned examplars of a class, such as faces, we automatically build an alignment mechanism, without any additional labeling of parts or poses in the data set. Using this alignment mechanism, new members of the class, such as faces resulting from a face detector, can be precisely aligned for the recognition process. Our alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm specifically developed for and trained on hand-labeled face images. We also demonstrate its use on an entirely different class of objects (cars), again without providing any information about parts or pose to the learning algorithm. <s> BIB015 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> The pupil localization algorithm is very important for a face recognition system. Traditional pupil localization algorithms are easy to be affected by uneven illuminations and accessories. Aiming at those limitations, a novel pupil localization algorithm is proposed in this paper. The algorithm firstly implements face image tilt adjustment and extracts eye region through the horizontal intensity gradient integral projection and Gabor filtering. Then in order to increase the eye detection accuracy, PCA is applied to select Gabor filter, and the projection enhancement algorithm is presented. At last the Negative Radial Symmetry is presented to locate the pupil position precisely in eye windows. Experimental results show that the method can locate the pupil position accurately, and demonstrate robustness to uneven lighting, noise, accessories and pose variations. <s> BIB016 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> In this paper, we propose a new descriptor for texture classification that is robust to image blurring. The descriptor utilizes phase information computed locally in a window for every image position. The phases of the four low-frequency coefficients are decorrelated and uniformly quantized in an eight-dimensional space. A histogram of the resulting code words is created and used as a feature in texture classification. Ideally, the low-frequency phase components are shown to be invariant to centrally symmetric blur. Although this ideal invariance is not completely achieved due to the finite window size, the method is still highly insensitive to blur. Because only phase information is used, the method is also invariant to uniform illumination changes. According to our experiments, the classification accuracy of blurred texture images is much higher with the new method than with the well-known LBP or Gabor filter bank methods. Interestingly, it is also slightly better for textures that are not blurred. <s> BIB017 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> A novel method for eye and mouth detection and eye center and mouth corner localization, based on geometrical information is presented in this paper. First, a face detector is applied to detect the facial region, and the edge map of this region is calculated. The distance vector field of the face is extracted by assigning to every facial image pixel a vector pointing to the closest edge pixel. The x and y components of these vectors are used to detect the eyes and mouth regions. Luminance information is used for eye center localization, after removing unwanted effects, such as specular highlights, whereas the hue channel of the lip area is used for the detection of the mouth corners. The proposed method has been tested on the XM2VTS and BioID databases, with very good results. <s> BIB018 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> This paper introduces a class of correlation filters called average of synthetic exact filters (ASEF). For ASEF, the correlation output is completely specified for each training image. This is in marked contrast to prior methods such as synthetic discriminant functions (SDFs) which only specify a single output value per training image. Advantages of ASEF training include: insensitivity to over-fitting, greater flexibility with regard to training images, and more robust behavior in the presence of structured backgrounds. The theory and design of ASEF filters is presented using eye localization on the FERET database as an example task. ASEF is compared to other popular correlation filters including SDF, MACE, OTF, and UMACE, and with other eye localization methods including Gabor Jets and the OpenCV cascade classifier. ASEF is shown to outperform all these methods, locating the eye to within the radius of the iris approximately 98.5% of the time. <s> BIB019 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> In this paper we address the problem of eye localization for the purpose of face matching in low and standard definition image and video content. In addition to an explorative study that aimed at discovering the effect of eye localization accuracy on face matching performance, we also present a probabilistic eye localization method based on well-known multi-scale local binary patterns (LBPs). These patterns provide a simple but powerful spatial description of texture, and are robust to the noise typical to low and standard definition content. The extensive evaluation involving multiple eye localizers and face matchers showed that the shape of the eye localizer error distribution has a big impact on face matching performance. Conditioned by the error distribution shape and the minimum required eye localization accuracy, eye localization can boost the performance of naive face matchers and allow for more efficient face matching without degrading its performance. The evaluation also showed that our proposed method has superior accuracy with respect to the state-of-the-art on eye localization, and that it fulfills the criteria for improving the face matching performance and efficiency mentioned above. <s> BIB020 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Face localization, feature extraction, and modeling are the major issues in automatic facial expression recognition. In this paper, a method for facial expression recognition is proposed. A face is located by extracting the head contour points using the motion information. A rectangular bounding box is fitted for the face region using those extracted contour points. Among the facial features, eyes are the most prominent features used for determining the size of a face. Hence eyes are located and the visual features of a face are extracted based on the locations of eyes. The visual features are modeled using support vector machine (SVM) for facial expression recognition. The SVM finds an optimal hyperplane to distinguish different facial expressions with an accuracy of 98.5%. <s> BIB021 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, kernel-based feature extraction and multiple feature fusion. Specifically, we make three main contributions: 1) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; 2) we introduce local ternary patterns (LTP), a generalization of the local binary pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions, and we show that replacing comparisons based on local spatial histograms with a distance transform based similarity metric further improves the performance of LBP/LTP based face recognition; and 3) we further improve robustness by adding Kernel principal component analysis (PCA) feature extraction and incorporating rich local appearance cues from two complementary sources-Gabor wavelets and LBP-showing that the combination is considerably more accurate than either feature set alone. The resulting method provides state-of-the-art performance on three data sets that are widely used for testing recognition under difficult illumination conditions: Extended Yale-B, CAS-PEAL-R1, and Face Recognition Grand Challenge version 2 experiment 4 (FRGC-204). For example, on the challenging FRGC-204 data set it halves the error rate relative to previously published methods, achieving a face verification rate of 88.1% at 0.1% false accept rate. Further experiments show that our preprocessing method outperforms several existing preprocessors for a range of feature sets, data sets and lighting conditions. <s> BIB022 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> This paper proposes a novel eye detection method, which can locate the accurate positions of the eyes from frontal face images. The proposed method is robust to pose changes, different facial expressions and illumination variations. Initially, it utilizes image enhancement, Gabor transformation and cluster analysis to extract eye windows. It then localizes the pupil centers by applying two neighborhood operators within the eye windows. Experiments with the color FERET and the LFW (Labeled Face in the Wild) datasets (including a total of 3587 images) are used to evaluate this method. The experimental results demonstrate the consistent robustness and efficiency of the proposed method. <s> BIB023 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> Focused on facial features localization on multi-view face arbitrarily rotated in plane, a novel detection algorithm based improved SVM is proposed. First, the face is located by the rotation invariant multi-view (RIMV) face detector and its pose in plane is corrected by rotation. After the searching ranges of the facial features are determined, the crossing detection method which uses the brow-eye and nose-mouth features and the improved SVM detectors trained by large scale multi-view facial features examples is adopted to find the candidate eye, nose and mouth regions,. Based on the fact that the window region with higher value in the SVM discriminant function is relatively closer to the object, and the same object tends to be repeatedly detected by near windows, the candidate eyes, nose and mouth regions are filtered and merged to refine their location on the multi-view face. Experiments show that the algorithm has very good accuracy and robustness to the facial features localization with expression and arbitrary face pose in complex background. <s> BIB024 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Preprocessing methods <s> A combined face and eye detector system based on multiresolution local ternary patterns and local phase quantization descriptors can achieve noticeable performance improvements by extracting features locally. <s> BIB025
The goal of preprocessing stage is to facilitate the task of feature extraction by removing noise information and narrowing down the search region. The first step for this is to locate and segment out faces from complex scenes with cluttered backgrounds. Although this can be done by hand, in most cases a modern face detector  will do it quickly and accurately. The output of a face detector is a rough face region possibly with different scales, rotations and lightings. Therefore it might be useful to remove these variations from the acquired faces prior to further analysis. Handling pose variations: pose variations due to scale changes and in-plane rotations are commonly handled by geometric transformation. In many face recognition applications this is performed based on the coordinates of some predefined landmarks like eyes or the nose, which are unfortunately assumed to be unknown in eye localization. Therefore warping methods which do not rely on such information are preferred for eye localization. In the ASM method  , a set of face images are simultaneously aligned using an iterative Procrustes algorithm which essentially aims to find the best affine transformation under a least square type criterion. Alternative normalization methods are based on the information theory. In BIB015 , the authors proposed an unsupervised method for face alignment with no need of landmarks by looking for a transformation for each face such that the empirical joint entropy is maximized after transforming. It is worth mentioning that the function of geometric normalization method should not be overstated and there exist alternative ways for this purpose. For example, to handle scale changes, one can scan the face images of different sizes by subsampling instead of doing geometric normalization. In practice, out-of-plane rotations with limited angle can be handled through geometric normalization, but the problem of compensating missing information for distorted face due to large in-depth rotation is quite difficult if no further prior knowledge is provided. Image enhancement: pixel values are raw intensity features and could be easily affected by illumination variations. Traditional approaches for dealing with this issue can be broadly classified into three categories: appearance-based methods, normalizationbased, and feature-based methods. In direct appearance-based approaches, training examples are collected under different lighting conditions used to learn a global model of the possible illumination variations, for example a linear subspace or manifold model, which then generalizes to the variations seen in new images BIB009 BIB014 . Direct learning of this kind of model makes few assumptions but it requires a large number of training images and an expressive feature set, therefore it is seldom used in the application of eye localization. Normalization based approaches seek to reduce the image to a more 'canonical' form in which the illumination variations are suppressed. Histogram equalization is one simple example, but purpose-designed methods often exploit the fact that (on the scale of a face) naturally-occurring incoming illumination distributions typically have predominantly low spatial frequency and soft edges so that high frequency information in the image is the predominant signal (i.e., intrinsic facial appearance). For example, the Multi-scale Retinex method of Jobson et al. BIB003 cancels much of the low frequency information by dividing the image by a smoothed version of itself. More recently, Gross and Brajovic (GB) BIB008 developed an anisotropic smoothing method that relies on the iterative estimation of a blurred version of the original image. In BIB022 , a signal processing approach motivated more by bottom-up human perception than by Retinex theory is presented. Overall, these methods are quite effective and efficient but the problem of handling spatially non-uniform variations remains an open problem. The third approach extracts illumination-insensitive feature sets directly from the given image. These feature sets range from geometrical features BIB001 to image derivative features such as edge maps BIB004 , Local Binary Patterns (LBP)  , Local Ternary Patterns (LTP) BIB022 , Local Phase Quantization (LPQ) BIB017 , Distance Vector Field (DVF, BIB018 ), Gabor wavelets BIB005 , and local autocorrelation filters BIB002 . For instance, Nanni and Lumini BIB025 built a well performed eye classification module based on multiresolution LTP BIB022 and LPQ BIB017 descriptors. Yang et al. BIB010 designed a 'Gabor-Eye' representation for eyes which exploits the frequency and direction sensitivity properties of Gabor feature sets. Qian and Xu BIB023 selected some Gabor-transformed images to reconstruct an intermediate representation suitable for eye localization, in which the signal of the eye region is enhanced and the influence of lighting variations is reduced. To stabilize the Gabor filter direction selection and increase the robustness to the accessories appearing on the eye-and-brow region, Xiong et al. BIB016 present a method based on the face tilt angle detection and adjustment techniques. Alternatively, Crowley et al. BIB011 used the first and second order Gaussian derivatives as filters to extract feature vectors which are robust to chrominance variations of light. Shrinking search areas: narrowing down the search region is helpful to improve both the efficiency and the effectiveness of eye localization in general. The search region plays the role of hypothesis space, reflecting the prior knowledge about the place where the eyes are assumed to lie. The size is crucial in setting the hypothesis space-a small one may simply not contain eyes and a large one may lead to unnecessarily computational cost. Usually a face detection operation is first performed to locate the face region then a search window is put at the eye region based on knowledge from the training set. This strategy works well in most cases, but care must be taken when the location of the eye is unpredictable (e.g., being occluded due to pose variations). In the later cases, expanding the search space to include the entire image might be a better choice BIB019 . Alternatively, one may progressively use different templates with different scales to narrow down the search space. For example, in BIB024 , a multi-view face detector is first used to estimate the rotation state of the face, based on which a coarse-to-fine strategy is further applied to locate brow-eye regions and the eyes respectively. In practice, some heuristics may be used to locate the search window. The simplest one is based on the fact that eyes are almost always lying in the upper part of a face and the intensity of eye center always has lower contrast compared to its surroundings. Such cues are very informative to give a coarse prediction of eye positions BIB012 . Actually one can even try to learn the prior distribution of coordinates of the eyes given a large number of samples BIB020 . Another useful heuristic is that the eye regions have low intensity, low red chrominance, and high blue chrominance, when compared to the forehead region of the face. This heuristic is commonly used to segment the eye region from the face image, followed by some morphological operations (e.g., dilation and erosion) to enhance certain interesting pixels of the eye region BIB007 BIB021 BIB006 . Other eye features described in Section 2.1, such as intensity contrast and edge map, can also be used to roughly locate the eye region efficiently. In BIB016 , candidate eye region is extracted through the horizontal intensity gradient integral projection and Gabor filtering. Campadelli and Lanzarotti BIB013 uses a man-made eye template to roughly locate 10 candidate eye regions and selects the best one based on the symmetry condition and majority voting methods.