A literature survey on robust and efficient eye localization in real-life scenarios <s> Measure metric <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> BIB001 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measure metric <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> BIB002 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measure metric <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> BIB003 </s> A literature survey on robust and efficient eye localization in real-life scenarios <s> Measure metric <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> BIB004
The design of the ground truth: the localization accuracy is basically measured by the distance between the predicted position and ground truth location of the eye. The ground truth is defined as a representation of the agreed correct result of the ideal eye localization method and forms the basis for all performance comparisons among the methods to be evaluated. Therefore, the design of the ground truth is crucial. We note, however, that this is task-specific. Take the task of gaze estimation for example. It would be reasonable to define the ground truth as the center of the pupil. While in the task of automatic face recognition, the procedure of eye localization mainly serves to provide the anchor information for the subsequent geometric normalization. But under the side-looking gaze direction while keeping frontal face pose, pupil positions will deviate away from eye center. In this case face will be misaligned if we still take pupil positions as reference points (see Fig. 12 ). What is more, labeling pupils is error-prone under uncontrolled conditions featured with closure eye states, poor image quality, glasses reflection, and eye partial occlusion. In these cases it would be better to take the actual center of the eye as its position. Recently, Kostinger et al. BIB004 propose a method which defines facial landmark positions based on a rigid 3D face model (see Fig. 13 ). One advantage of this method lies in its capability to deal with the variability due to face pose changes. It has been successfully used to label the centers of the eyes and other facial landmarks on a large scale face database BIB004 . Localization error measurement: after defining the ground truth, the next step is to measure the localization error. Among others, most commonly used measurement is the normalized eye localization error proposed in BIB001 , which is defined in terms of the eye center positions according to where C l and C r are the ground-truth positions and d l and d r are the Euclidean distances between the detected eye centers and the For the face under out-of-plane rotation, the two eyes distance cannot reflect the actual face scale, and the normalized localization error measurement may be biased. In such case more general evaluation measures such as mean and variance could be used BIB003 . Alternatively, depending on where the localization system is used, the performance can also be evaluated by checking how well the interested task is solved (e.g., in terms of the improvement on the accuracy of face recognition BIB002 ).