A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> The synthesis of a new category of spatial filters that produces sharp output correlation peaks with controlled peak values is considered. The sharp nature of the correlation peak is the major feature emphasized, since it facilitates target detection. Since these filters minimize the average correlation plane energy as the first step in filter synthesis, we refer to them as minimum average correlation energy filters. Experimental laboratory results from optical implementation of the filters are also presented and discussed. <s> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> A mathematical analysis of the distortion tolerance in correlation filters is presented. A good measure for distortion performance is shown to be a generalization of the minimum average correlation energy criterion. To optimize the filter's performance, we remove the usual hard constraints on the outputs in the synthetic discriminant function formulation. The resulting filters exhibit superior distortion tolerance while retaining the attractive features of their predecessors such as the minimum average correlation energy filter and the minimum variance synthetic discriminant function filter. The proposed theory also unifies several existing approaches and examines the relationship between different formulations. The proposed filter design algorithm requires only simple statistical parameters and the inversion of diagonal matrices, which makes it attractive from a computational standpoint. Several properties of these filters are discussed with illustrative examples. <s> BIB002 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> Eye location is used as a test bed for developing navigation routines implemented as visual routines within the framework of adaptive behavior-based AI. The adaptive eye location approach seeks first where salient objects are, and then what their identity is. Specifically, eye location involves: 1) the derivation of the saliency attention map, and 2) the possible classification of salient locations as eve regions. The saliency ("where") map is derived using a consensus between navigation routines encoded as finite-state automata exploring the facial landscape and evolved using genetic algorithms (GAs). The classification ("what") stage is concerned with the optimal selection of features, and the derivation of decision trees, using GAs, to possibly classify salient locations as eyes. The experimental results, using facial image data, show the feasibility of our method, and suggest a novel approach for the adaptive development of task-driven active perception and navigational mechanisms. <s> BIB003 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <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> BIB004 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> We present a method for precise eye localization that uses two Support Vector Machines trained on properly selected Haar wavelet coefficients. The evaluation of our technique on many standard databases exhibits very good performance. Furthermore, we study the strong correlation between the eye localization error and the face recognition rate. <s> BIB005 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> In this paper, we propose a new method for training mixtures of linear SVM classifiers for purposes of non-linear data classification. We do this by packaging linear SVMs into a probabilistic formulation and embedding them in the mixture of experts model. The weights of the mixture model are generated by the gating network dependent on the input data. The new mixture of linear SVMs can be then trained efficiently using the EM algorithm. Unlike previous SVM-based mixture of expert models, which use a divide-and-conquer strategy to reduce the burden of training for large scale data sets, the main purpose of our approach is to improve the efficiency for testing. Experimental results show that our proposed model can achieve the efficiency of linear classifiers in the prediction phase while still maintaining the classification performance of nonlinear classifiers. <s> BIB006 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform localization, one can take a sliding window approach, but this strongly increases the computational cost, because the classifier function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch-and-bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. It converges to a globally optimal solution typically in sublinear time. We show how our method is applicable to different object detection and retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest neighbor classifiers based on the chi2-distance. We demonstrate state-of-the-art performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC 2006 dataset and in the PASCAL VOC 2007 competition. <s> BIB007 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> Salient areas in natural scenes are generally regarded as the candidates of attention focus in human eyes, which is the key stage in object detection. In computer vision, many models have been proposed to simulate the behavior of eyes such as SaliencyToolBox (STB), neuromorphic vision toolkit (NVT) and etc., but they demand high computational cost and their remarkable results mostly rely on the choice of parameters. Recently a simple and fast approach based on Fourier transform called spectral residual (SR) was proposed, which used SR of the amplitude spectrum to obtain the saliency map. The results are good, but the reason is questionable. <s> BIB008 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> Local experts have been used to great effect for fitting deformable models to images. Typically, the best location in an image for the deformable model's landmarks are found through a locally exhaustive search using these experts. In order to achieve efficient fitting, these experts should afford an efficient evaluation, which often leads to forms with restricted discriminative capacity. In this work, a framework is proposed in which multiple simple experts can be utilized to increase the capacity of the detections overall. In particular, the use of a mixture of linear classifiers is proposed, the computational complexity of which scales linearly with the number of mixture components. The fitting objective is maximized using the expectation maximization (EM) algorithm, where approximations to the true objective are made in order to facilitate efficient and numerically stable fitting. The efficacy of the proposed approach is evaluated on the task of generic face fitting where performance improvement is observed over two existing methods. <s> BIB009 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> The literature on the topic has shown a strong correlation between the degree of precision of face localization and the face recognition performance. Hence, there is a need for precise facial feature detectors, as well as objective measures for their evaluation and comparison. In this paper, we will present significant improvements to a previous method for precise eye center localization, by integrating a module for mouth localization. The technique is based on Support Vector Machines trained on optimally chosen Haar wavelet coefficients. The method has been tested on several public databases; the results are reported and compared according to a standard error measure. The tests show that the algorithm achieves high precision of localization. <s> BIB010 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <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> BIB011 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <s> In recent years, sparse representation originating from signal compressed sensing theory has attracted increasing interest in computer vision research community. However, to our best knowledge, no previous work utilizes L1-norm minimization for human detection. In this paper we develop a novel human detection system based on L1-norm Minimization Learning (LML) method. The method is on the observation that a human object can be represented by a few features from a large feature set (sparse representation). And the sparse representation can be learned from the training samples by exploiting the L1-norm Minimization principle, which can also be called feature selection procedure. This procedure enables the feature representation more concise and more adaptive to object occlusion and deformation. After that a classifier is constructed by linearly weighting features and comparing the result with a calculated threshold. Experiments on two datasets validate the effectiveness and efficiency of the proposed method. <s> BIB012 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Accuracy and efficiency tradeoffs <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> BIB013
Localizing eyes from faces in real time is crucial to many practical applications. The efficiency depends not only on the eye prediction model but also on the search and matching strategy applied. Sliding window technique is commonly used for object detection, while the strategy of 'coarse-to-fine' is usually adopted to achieve better tradeoff between accuracy and efficiency. To solve the high accuracy and low complexity dilemma, and considering that most search regions are backgrounds, it is wise to use discriminant model but with less complexity to quickly filter out noise regions and use more complex model on most probable eye regions. Campadelli et al. BIB005 build two cascading SVM classifiers with different complexities and accuracies to achieve accurate eye localization in coarse-to-fine way. On the other hand, more efficient eye model design is helpful. In the above introduced cascading classifiers, the last level classifiers usually have high accuracy but also have high model complexity; the time cost is still expensive even evaluating in the reduced search space. Many factors contribute to the complexity of models and there are many ways to measure the complexity in literatures, such as VC dimension and Minimum Description Length (MDL), but it is generally agreed that the degree of complexity increases with the number and magnitude of the effective parameters involved. One way to achieve high accuracy but with controllable complexity is to linearly integrate a few of simple classifiers, as does in the AdaBoost framework for object detection  . Similarly, BIB006 linearly combined a set of linear SVMs, which are shown to have equivalent accuracy to nonlinear SVM, but with less model complexity. Saragih et al. BIB009 extended this idea to implement an efficient ASM model for facial feature localization. Recently, compress sensing techniques originated from the field of signal processing are popularly applied to select the most informative feature set and hence reduce the complexity of the final model in object detection BIB012 . Others propose to use machine learning methods like Fisher discriminant analysis and SVM to extract/select discriminative feature sets so as to improve the efficiency and effectiveness of eye localization BIB004 BIB010 . The drawback of sliding windows technique is the huge size of the search space. To overcome this, researchers try some interesting ways that go beyond sliding window technique. For instance, Lampert et al. BIB007 propose the Efficient Subwindow Search method, which adopts a branch-and-bound scheme to find the global optimum of the quality function over all sub-images, and runs in linear time. This method has recently been extended in the context of Hough voting for object localization BIB013 . On the other hand, some methods are motivated by the efficient visual attention mechanism of human visual system. For instance, inspired by the concept of visual routines  , Huang and Wechsler BIB003 proposed an eye localization method using navigational routines. They automated the derivation of such routines using evolution and learning, which effectively filters out unlikely eye locations and limits the use of the more expensive classifier. Alternatively, BIB008 implemented the attention mechanisms using the phase spectrum information of Fourier transform. The Correlation filter methods BIB001 BIB002 BIB011 effectively avoid exhaustively searching in the image domain by taking advantage of the fact that the correlation computation in the image domain is equivalent to element-wise multiplication in Fourier domain.