A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing 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> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing methods <s> In this paper, we present a novel face detection approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns, rotated up to /spl plusmn/20 degrees in image plane and turned up to /spl plusmn/60 degrees, in complex real world images. The proposed system automatically synthesizes simple problem-specific feature extractors from a training set of face and nonface patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. The face detection procedure acts like a pipeline of simple convolution and subsampling modules that treat the raw input image as a whole. We therefore show that an efficient face detection system does not require any costly local preprocessing before classification of image areas. The proposed scheme provides very high detection rate with a particularly low level of false positives, demonstrated on difficult test sets, without requiring the use of multiple networks for handling difficult cases. We present extensive experimental results illustrating the efficiency of the proposed approach on difficult test sets and including an in-depth sensitivity analysis with respect to the degrees of variability of the face patterns. <s> BIB002 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing 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> BIB003 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing methods <s> This paper presents a novel approach for eye detection using a hierarchy cascade classifier based on Adaboost statistical learning method combined with SVM (Support Vector Machines) post classifier. On the first stage a face detector is used to locate the face in the whole image. After finding the face, an eye detector is used to detect the possible eye candidates within the face areas. Finally, the precise eye positions are decided by the eye-pair SVM classifiers which using geometrical and relative position information of eye-pair and the face. Experimental results show that this method can effectively cope with various image conditions and achieve better location performance on diverse test sets than some newly proposed methods. <s> BIB004 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing methods <s> The accuracy of face alignment affects the performance of a face recognition system. Since face alignment is usually conducted using eye positions, an accurate eye localization algorithm is therefore essential for accurate face recognition. In this paper, we first study the impact of eye locations on face recognition accuracy, and then introduce an automatic technique for eye detection. The performance of our automatic eye detection technique is subsequently validated using FRGC 1.0 database. The validation shows that our eye detector has an overall 94.5% eye detection rate, with the detected eyes very close to the manually provided eye positions. In addition, the face recognition performance based on the automatic eye detection is shown to be comparable to that of using manually given eye positions. <s> BIB005 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing methods <s> We present a novel method for localizing faces in person identification scenarios. Such scenarios involve high resolution images of frontal faces. The proposed algorithm does not require color, copes well in cluttered backgrounds, and accurately localizes faces including eye centers. An extensive analysis and a performance evaluation on the XM2VTS database and on the realistic BioID and BANCA face databases is presented. We show that the algorithm has precision superior to reference methods. <s> BIB006 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing methods <s> We address the task of accurately localizing the eyes in face images extracted by a face detector, an important problem to be solved because of the negative effect of poor localization on face recognition accuracy. We investigate three approaches to the task: a regression approach aiming to directly minimize errors in the predicted eye positions, a simple Bayesian model of eye and non-eye appearance, and a discriminative eye detector trained using AdaBoost. By using identical training and test data for each method we are able to perform an unbiased comparison. We show that, perhaps surprisingly, the simple Bayesian approach performs best on databases including challenging images, and performance is comparable to more complex state-of-the-art methods. <s> BIB007 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing methods <s> This paper presents a novel method for detecting and localizing objects of a visual category in cluttered real-world scenes. Our approach considers object categorization and figure-ground segmentation as two interleaved processes that closely collaborate towards a common goal. As shown in our work, the tight coupling between those two processes allows them to benefit from each other and improve the combined performance. ::: ::: The core part of our approach is a highly flexible learned representation for object shape that can combine the information observed on different training examples in a probabilistic extension of the Generalized Hough Transform. The resulting approach can detect categorical objects in novel images and automatically infer a probabilistic segmentation from the recognition result. This segmentation is then in turn used to again improve recognition by allowing the system to focus its efforts on object pixels and to discard misleading influences from the background. Moreover, the information from where in the image a hypothesis draws its support is employed in an MDL based hypothesis verification stage to resolve ambiguities between overlapping hypotheses and factor out the effects of partial occlusion. ::: ::: An extensive evaluation on several large data sets shows that the proposed system is applicable to a range of different object categories, including both rigid and articulated objects. In addition, its flexible representation allows it to achieve competitive object detection performance already from training sets that are between one and two orders of magnitude smaller than those used in comparable systems. <s> BIB008 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Postprocessing methods <s> This paper addresses the task of efficient object class detection by means of the Hough transform. This approach has been made popular by the Implicit Shape Model (ISM) and has been adopted many times. Although ISM exhibits robust detection performance, its probabilistic formulation is unsatisfactory. The PRincipled Implicit Shape Model (PRISM) overcomes these problems by interpreting Hough voting as a dual implementation of linear sliding-window detection. It thereby gives a sound justification to the voting procedure and imposes minimal constraints. We demonstrate PRISM's flexibility by two complementary implementations: a generatively trained Gaussian Mixture Model as well as a discriminatively trained histogram approach. Both systems achieve state-of-the-art performance. Detections are found by gradient-based or branch and bound search, respectively. The latter greatly benefits from PRISM's feature-centric view. It thereby avoids the unfavourable memory trade-off and any on-line pre-processing of the original Efficient Subwindow Search (ESS). Moreover, our approach takes account of the features' scale value while ESS does not. Finally, we show how to avoid soft-matching and spatial pyramid descriptors during detection without losing their positive effect. This makes algorithms simpler and faster. Both are possible if the object model is properly regularised and we discuss a modification of SVMs which allows for doing so. <s> BIB009
For those methods localizing eyes localization through detection BIB007 BIB004 BIB005  BIB001 , a postprocessing step is usually needed to further evaluate the most probable eye positions or return feedbacks to final decision. One reason for this is that they commonly use a classifier like SVM or AdaBoost to find the best decision boundary which discriminates the positive samples from negative ones in terms of classification accuracy rather than optimizing the objective to give highest score to the true object of interest at the right position. Besides, the ground truth with small localization errors may seriously mislead the learning procedure of classifier BIB007 . Therefore, we should not over-interpret the output of the classifier-it is just an indicator about whether or not eyes are probably present at some positions, but not necessarily their exact positions. Actually, due to the spatial dependence between candidate patches, it is very likely that the eye detector yields several positive responses with similar scores in the vicinity of some locations. Consequently, postprocessing like non-maximal suppression is needed to remove such ambiguity. However, it is worth mentioning that unlike postprocessing in the general context of object localization where multiple instances may exist simultaneously in one image, for eyes we know a prior that only one eye exists in one search window. One of the best known non-maximal suppression methods is developed by Viola and Jones in their face detector  , in which highly overlapping detections are merged into one representative face using a clustering algorithm. To this end, various heuristics can be used to fine localization and false alarm dismissal. Garcia and Delakis BIB002 interpreted the fine localization as a local search procedure and proposed to accumulate evidences for positives in the scale space, based on the observation that true objects usually give a significant number of high positive responses in consecutive scales, which is not often the case for negatives. However, in some Hough voting methods (e.g., ISM BIB008 , PRISM BIB009 ) the searching space is modeled as continuous rather than discrete, which makes the searching become much more complicated. Many efforts have been made to address this issue BIB008 BIB009 as reviewed in the previous sections. The anthropometric measures are also frequently used for postprocessing, due to the fact that the spatial structures between two eyes and other facial features could pose rather strong (and useful) constraints on the locations of the eyes. Actually, most of the methods reviewed in Section 2.3 can be thought in this waythey simply combine the task of eye detection and fine localization into one single objective function. But many methods do perform their postprocessing explicitly. In  , the authors used an AdaBoost detector to segment the eye region and then a fast radial symmetry operator to finely locate the centers of the eyes. However, this geometric constraint also implies that it can only be used for open eyes. To leverage on the effectiveness of postprocessing, BIB004 relaxed the constraint criteria of the AdaBoost eye model so as to generate more candidates for further verification. Instead of using a single-eye patch, patches containing both eyes could be utilized to train the SVM classifier as well BIB001 , which implicitly exploits the spatial dependence between two eyes. To eliminate small spurious detected regions, Crowley et al. BIB003 used a connected components analysis algorithm and computed the bounding box around. Regions with a small bounding box are eliminated. Hamouz et al. BIB006 described a different method for postprocessing by checking the consistency of candidates in a lower dimensional space. In particular, they first transform all the data into a canonical space and then find the best candidate there using an SVM. The canonical space is trained with ground truth and hence serves to encode the constraints about what the 'good' samples should look like. This idea is similar to that in ASM  , which recommends a better shape vector for face alignment in a pre-learned shape space given the current fitness.