A literature survey on robust and efficient eye localization in real-life scenarios <s> Databases and performance evaluation <s> Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1199 individuals are included in the FERET database, which is divided into development and sequestered portions. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to (1) assess the state of the art, (2) identify future areas of research, and (3) measure algorithm performance on large databases. <s> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Databases and performance evaluation <s> As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered. <s> BIB002 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Databases and performance evaluation <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> BIB003 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Databases and performance evaluation <s> Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery. <s> BIB004 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Databases and performance evaluation <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> BIB005 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Databases and performance evaluation <s> We have created the first image search engine based entirely on faces. Using simple text queries such as "smiling men with blond hair and mustaches," users can search through over 3.1 million faces which have been automatically labeled on the basis of several facial attributes. Faces in our database have been extracted and aligned from images downloaded from the internet using a commercial face detector, and the number of images and attributes continues to grow daily. Our classification approach uses a novel combination of Support Vector Machines and Adaboost which exploits the strong structure of faces to select and train on the optimal set of features for each attribute. We show state-of-the-art classification results compared to previous works, and demonstrate the power of our architecture through a functional, large-scale face search engine. Our framework is fully automatic, easy to scale, and computes all labels off-line, leading to fast on-line search performance. In addition, we describe how our system can be used for a number of applications, including law enforcement, social networks, and personal photo management. Our search engine will soon be made publicly available. <s> BIB006 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Databases and performance evaluation <s> Face alignment is a crucial step in face recognition tasks. Especially, using landmark localization for geometric face normalization has shown to be very effective, clearly improving the recognition results. However, no adequate databases exist that provide a sufficient number of annotated facial landmarks. The databases are either limited to frontal views, provide only a small number of annotated images or have been acquired under controlled conditions. Hence, we introduce a novel database overcoming these limitations: Annotated Facial Landmarks in the Wild (AFLW). AFLW provides a large-scale collection of images gathered from Flickr, exhibiting a large variety in face appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total 25,993 faces in 21,997 real-world images are annotated with up to 21 landmarks per image. Due to the comprehensive set of annotations AFLW is well suited to train and test algorithms for multi-view face detection, facial landmark localization and face pose estimation. Further, we offer a rich set of tools that ease the integration of other face databases and associated annotations into our joint framework. <s> BIB007
Although there are many face databases BIB002 developed, they are not originally aiming for the problem of eye localization and are collected under well controlled laboratory conditions with normal lighting, neutral expression and high image quality. Despite these drawbacks, they contain many kinds of interesting eye pattern variations and hence are widely used for the eye localization research nowadays. Among others, popular face databases include FERET BIB001 , FRGC (Face Recognition Grand Challenge, BIB004 ), JAFFE [20] , BioID [23], LFW (Labeled Face in the Wild,  ) , FaceTracer BIB006 and so on. Fortunately, most of these databases are shipped with the ground truth of eye positions and thus can be used for model training. Alternatively, plenty of sources for reliable labels of eyes are publicly available in the area of face alignment. For details, see Kostinger et al. BIB007 . More information about published miscellaneous face databases can be found in the Internet  , and some useful sources are listed in Table 2 . Table 3 summarizes the performance evaluated on some of these databases and Fig. 14 gives a visual illustration of the localization results on them. In the table we highlight the major challenges contained in each database, the size of test images, the localization performance in terms of the percentage of images that have been successfully handled corresponding to a certain localization error (cf. Eq. (2)). Note also that the time costs given in this table may be recorded under different settings. Among them, FERET BIB001 is one of the most popular databases for both face recognition and eye localization. Face images in this database are taken under controlled conditions, thus they are suitable for general evaluation of eye localization algorithms. The table shows that most methods perform well on this database. The face images of JAFFE [20] have rich expression variations and most with eyes wide open, which makes them particularly suitable for evaluating those methods based on measuring the geometrical or intensity characteristics of eyes (e.g.,  ). The BioID is a much more challenging database for eye localization, featured with a large variety of changes in illumination, background and face size. Actually, performance reported on this database drops about 7% compared to that on the relatively simple databases BIB003 BIB005 . This indicates that complicated uncontrolled conditions are still big challenges for current eye localization methods. In this aspect, methods exploiting rich feature sets and structural information may have advantages (cf. Fig. 14) . Finally, the FRGC BIB004 face data are featured with large lighting changes but with high image resolution, and most methods show satisfying performance on this database.