A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> Few approaches to automated facial recognition have employed geometric measurement of characteristic features of a human face. Eye spacing measurement has been identified as an important step in achieving this goal. Measurement of spacing has been made by application of the Hough transform technique to detect the instance of a circular shape and of an ellipsoidal shape which approximate the perimeter of the iris and both the perimeter of the sclera and the shape of the region below the eyebrows respectively. Both gradient magnitude and gradient direction were used to handle the noise contaminating the feature space. Results of this application indicate that measurement of the spacing by detection of the iris is the most accurate of these three methods with measurement by detection of the position of the eyebrows the least accurate. However, measurement by detection of the eyebrows' position is the least constrained method. Application of these techniques has led to measurement of a characteristic feature of the human face with sufficient accuracy to merit later inclusion in a full package for automated facial recognition. <s> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> Abstract A model-based methodology is proposed to detect facial features from a front-view ID-type picture. The system is composed of three modules: context (i.e. face location), eye, and mouth. The context module is a low resolution module which defines a face template in terms of intensity valley regions. The valley regions are detected using morphological filtering and 8-connected blob coloring. The objective is to generate a list of hypothesized face locations ranked by face likelihood. The detailed analysis is left for the high resolution eye and mouth modules. The aim for both is to confirm as well as refine the locations and shapes of their respective features of interest. The detection is done via a two-step modelling approach based on the Hough transform and the deformable template technique. The results show that facial features can be located very quickly with Adequate or better fit in over 80% of the images with the proposed system. <s> BIB002 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> Abstract An improved method of extracting eye features from facial images using eye templates is described. It retains all advantages of the deformable template method originally proposed by A. L. Yuille, P. W. Hallinan and D. S. Cohen ( Int. J. Comput. Vision 99–111 (1989)) and rectifies some of its weaknesses. This is achieved by the following modifications. First, the original eye template and the overall energy function to represent the most salient features of the eye are modified. Secondly, in order to simplify the issue of selecting weights for the energy terms, the value of each energy term is normalized in the range 0–1 and only two different weights are assigned. This weighting schedule does not require expert knowledge therefore it is more user friendly. Thirdly, all parameters of the template are changed simultaneously during the minimization process rather than using a sequential procedure. This scheme prevents some parameters of the eye template from being overly changed, helps the algorithm to converge to the global minimum, and reduces the processing time. The selection of initial parameters of the eye template is based on an eye window obtained in preprocessing. Experimental results are presented to demonstrate the efficacy of the algorithm. A comparison study of various processing schemes is also given. <s> BIB003 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> Facial feature extraction is an important step in automated visual interpretation and human face recognition. Among the facial features, the eye plays the most important part in the recognition process. The deformable template can be used in extracting the eye boundaries. However, the weaknesses of the deformable template are that the processing time is lengthy and that its success relies on the initial position of the template. In this paper, the head boundary is first located in a head-and-shoulders image. The approximate positions of the eyes are estimated by means of average anthropometric measures. Corners, the salient features of the eyes, are detected and used to set the initial parameters of the eye templates. The corner detection scheme introduced in this paper can provide accurate information about the corners. Based on the corner positions, we can accurately locate the templates in relation to the eye images and greatly reduce the processing time for the templates. The performance of the deformable template is assessed with and without using the information on corner positions. Experiments show that a saving in execution time of about 40% on average and a better eye boundary representation can be achieved by using the corner information. <s> BIB004 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> We propose an improved method for eye-feature extraction, descriptions, and tracking using deformable templates. Some existing algorithms are exploited to locate the initial position of eye features and then deformable templates are used for extracting and describing the eye features. Rather than using original energy minimization for matching the templates, the region-based approach is proposed for template deformation. Based on the region properties, the new strategy avoids problems such as template shrinking, adjusting the weights of energy terms, failure of orientation adjustment due to some exceptional cases. Our strategies are also coupled with Canny edge operator to give a new back-end processing. By integrating the local edge information from the edge detection and the global collector from our region-based template deformation, this processing stage can generate accurate eye-feature descriptions. Finally, the template deformation process is applied to tracking eye features. <s> BIB005 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> Abstract A computer vision based approach for human–computer interaction through head movements is presented and evaluated in a non-immersive virtual reality context. Once intercepted and tracked in real-time using a piecewise affine camera model and affine-deformable eye contours, user head displacements are estimated and remapped onto the tridimensional graphic environment according to a natural interface metaphor. Both the real-time performance of the tracker and the improved head parameter estimation accuracy – as compared to the one obtainable using globally affine camera models – encourage the use of this approach to support diverse advanced interaction scenarios and applications. <s> BIB006 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> The localization of human faces in digital images is a fundamental step in the process of face recognition. This paper presents a shape comparison approach to achieve fast, accurate face detection that is robust to changes in illumination and background. The proposed method is edge-based and works on grayscale still images. The Hausdorff distance is used as a similarity measure between a general face model and possible instances of the object within the image. The paper describes an efficient implementation, making this approach suitable for real-time applications. A two-step process that allows both coarse detection and exact localization of faces is presented. Experiments were performed on a large test set base and rated with a new validation measurement. <s> BIB007 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> A method for detecting and describing the features of faces using deformable templates is described. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image, by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parameter values can be used as descriptors for the features. This method is demonstrated by showing deformable templates detecting eyes and mouths in real images. > <s> BIB008 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measuring eye characteristics <s> We present a novel Hough voting-based method to improve the efficiency and accuracy of fiducial points localization, which can be conveniently integrated with any global prior model for final face alignment. Specifically, two or more stable facial components (e.g., eyes) are first localized and fixed as anchor points, based on which a separate local voting map is constructed for each fiducial point using kernel density estimation. The voting map allows us to effectively constrain the search region of fiducial points by exploiting the local spatial constraints imposed by it. In addition, a multioutput ridge regression method is adopted to align the voting map and the response map of local detectors to the ground truth map, and the learned transformations are then exploited to further increases the robustness of the algorithm against various appearance variations. Encouraging experimental results are given on several publicly available face databases. <s> BIB009
The idea of this line of research is to explore the distinct inherent features of eyes by treating eyes as a special facial feature by itself. Many eye-specific characteristics are ready to use in practice, such as the shape of the eyes and the intensity contrast between the eye white and the pupil. Once founded, these characteristics could be very reliable indicators of eyes. However, under the uncontrolled conditions, the measured characteristics tend to be less reliable, which may result in great performance loss. In addition, some characteristics, such as bright spot for infrared eye images, depend heavily on extra hardware devices and usually require active human cooperation.  , from left to right: variations in occlusion, pose, lighting, expression, and blur. 2.1.1. Shape characteristics Fig. 2 gives a typical shape model of eyes consisting of four major components: eyelids, the eye white, the iris and the pupil. Each of these components has a special geometric shape, e.g., elliptical shape for eyelids, circular shape for the iris and the pupil. Generally, there are two ways to represent these shapes, i.e., continuous or discrete. The representative work of the continuous shape model is Yuille et al.'s deformable model BIB008 , while Active Shape Model (ASM)  represents the eyes in a discrete way. In particular, Yuille et al. BIB008 built a parameterized deformable model which gives a continuous mathematical formulation of the shapes involved. More than that, their model takes into account the relevant features such as peaks and valleys of the accumulated intensity in the regions bounded by shapes. They have to search through a large continuous parameter space which covers almost all reasonable shape variations to fit the model to a testing image. For a good fitting in practice, two factors are of importance BIB008 BIB003 BIB004 : (1) the distinctness and flexibility of the shape model, i.e., the model should expressive enough to explain large reasonable variability of shape while suppresses impractical deformations; (2) a properly initialized template. In BIB002 BIB005 BIB006 , model parameters are redesigned to improve the discriminant and flexibility of deformable model, while others BIB003 BIB004 propose to improve the quality of the initialization by localizing eye corners first. Despite these improvements, deformable models still suffer from their large search space and their dependence on good image quality and good initialization. Another way to model the eye shape is to construct a statistical model for all the allowable shapes based on a series of discrete landmarks on the eyes. The Active Shape Model (ASM)  is the representative, which will be discussed in more detail in Section 2.3. Besides building a global shape model for eyes, it is possible to learn different local shape models for different eye components, e.g., for the pupil and the iris. In BIB001 , Hough transform was utilized to detect the circle shape of the eyes, and BIB007 proposed a neural network-based method with a compact circular perception fields for eye localization. Since eye corners are less vulnerable to the changes of eye states, they are sometimes used to improve the initialization accuracy of the deformable model BIB003 BIB004 BIB009 (cf. Fig. 4 ). For example, BIB009 presented a local Hough voting based method for face alignment, using the spatial constraints imposed by the stable facial components to guide the search of other facial points.