Robust Eye Detection Using Self Quotient image

Eye center localization in a facial image based on geometric shapes of iris and eyelid under natural variability

Face Recognition Across Gender Transformation Using SVM Classifier

Face recognition in the SWIR band when using single sensor multi-wavelength imaging systems

In order to investigate whether or not checkpoints of features surrounding the eye are suitable for eye tracking, we evaluated the checkpoints using statistical hypothesis testing. By combining the checkpoints of features surrounding the eye, the eye tracking system using the template matching was effective for the eye detection. The parameters of the checkpoints were empirically determined to detect the eye in any individual. The statistical hypothesis testing revealed that the empirical setting values of the checkpoints were significantly different from actual facial features obtained using face images. We investigated suitable parameters of the checkpoints derived from actual facial features obtained with facial images. It was revealed that new checkpoint settings were effective in drastically reducing the possibility of incorrect eye detection, although the correct detection rate was slightly decreased.

The process of eye tracking has been subject of intensive research during the last years. The main aim consists in the determination of the point of gaze or the eye movement relative to the head. In this work, we use a static video camera to realize the tracking of the eyes movement mainly due to its important features such as low cost, fully automated and easy application. The proposed approach encompasses a solution to the problems of eye detection and eye tracking. Our eye detection method employs a set of cascaded Haar classifiers that enable the fast recognition of eye regions in a real-time sequence of images. On the other hand, the eye tracking method uses particle filtering that is robust in clutter environments and allows the fusion of sensor data as well as the tracking of nonlinear systems. The results demonstrate that the eye tracking system is capable of robustly track the eyes under variable light conditions and under several different face orientations.

This study studies different lighting processing and feature extraction methods for efficient face recognition. The purpose is to find some robust face recognition methods by combining different feature extraction methods with illumination compensation. In this study, some typical illumination preprocessing approaches are reviewed including wavelet transformation, self‐quotient image, Retinex, smoothing, discrete cosine transform normalisation in logarithm domain, homomorphic filter and local contrast enhancement. As the main contribution, this study proposes two efficient feature extraction methods for face recognition. One is an adaptive feature extraction (AFE) based on curvelet transform. The other is a feature extraction technique named two‐dimensional principal component analysis (2DPCA) non‐parametric analysis of 2D subspace (2DPCA + 2DNSA). Two groups of experiments are designed to verify the proposed methods. The first group of experimental results show that the proposed AFE methods have better performance than conventional methods. In the second group of experiments, each feature extraction method is combined with nine different lighting processing methods. The results show that the proposed 2DPCA + 2DNSA method is more robust to lighting processing than other methods. Experimental results also show that lighting processing contribution to face recognition are quite different for different face databases.

Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But, such analyzes have rarely included Thermal (T) modality. This paper paves the way for performing such facial analyzes using synchronized RGB-D-T facial images by introducing a database of 51 persons including facial images of different rotations, illuminations, and expressions. Furthermore, a face recognition algorithm has been developed to use these images. The experimental results show that face recognition using such three modalities provides better results compared to face recognition in any of such modalities in most of the cases.

Face recognition has been a major research theme over the last two decades. There are several problems to be solved to improve the performance of face recognition. Such major problems involve appearance variation due to pose, illumination, expression, and aging. In particular, aging includes internal and external factors that cause facial appearance variation and, consequently, it is the most difficult problem to handle. In this paper, we propose a face recognition method that is robust against facial appearance variation due to aging. The proposed method employs segmentation verification of frontal face images that consists of the following three steps. (1) Face image segmentation generates three regional subimages from the input face image. (2) A matching score is calculated using gradient features from a pair consisting of the input image and a registered image for each of the three generated subimages and original (whole face) image. We obtain four matching scores. (3) The verifying classifier evaluates the matching score vector formed of the matching scores calculated for each of the four images and predicts the a posteriori probability that two matching images belong to the same person. The results of an experimental evaluation with the FGNET and MORPH face aging datasets clarify the effectiveness of the proposed method for age invariant face recognition.

A novel approach for eye detection is proposed, where an eye filter considering textural characteristics of eye regions and non-negative matrix factorisation (NMF)-based image reconstruction is utilised. The eye filter for detecting pairs of eye candidate points is utilised. To detect the exact eye position, all pairs of the eye candidates are evaluated by a cost function, which minimises the NMF-based image reconstruction error. Experimental results have shown that the proposed method achieves better performance of eye detection than existing methods under various environments such as head pose, facial expression and illumination conditions.

Human face recognition is still a challenging task for computer-based machines mainly due to the similarity of human faces and unpredictable variations such as illumination, pose, expression, occlusion etc. A lot of algorithms have been developed to deal with these challenging issues in face recognition. One of the difficult problems is the geometric variation of face images. In some face recognition applications, only face images with uncontrolled format are available. Face images obtained from face detection stage may be directly used. Therefore, alignment or normalization is required to achieve better recognition performance. Eye detection technique is normally used to normalize face images. Invariant representations are used to improve the generalization of facial features. Feature-based and modelbased automatic face alignment can also be used to localize human faces more precisely. Illumination variation is another tough problem because human face is a 3D model and practical lighting conditions are complicated and unpredictable. Illumination normalization based on preprocessing, illumination invariant feature extraction and face modelling have been employed to reduce illumination variations or render faces under varying lighting conditions. The geometric on-the-plane rotation of a face image can be normalized by aforementioned methods. However, a face image with rotation in depth is not easily adjusted to a certain view. It is difficult to recognize faces in all poses with only training face images in limited views. One solution is to use 2D or 3D generative models to generate novel views of the face. Another method is based on the view-subspace learning. For facial expression variations,

This paper presents eye detection method for gaze tracking system using the features of eye and corneal reflection. The proposed method sequentially discards the regions that are expected not to be an eye by classifiers based on the features such as pupil intensity and appearance, corneal reflection intensity, and so on. The classifiers are designed with some empirical parameters, thus the proposed method does not need training process.

Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

Traditional gaze tracking systems rely on explicit infrared lights and high resolution cameras to achieve high performance and robustness. These systems, however, require complex setup and thus are restricted in lab research and hard to apply in practice. In this paper, we propose to perform gaze tracking with a consumer level depth sensor (Kinect). Leveraging on Kinect's capability to obtain 3D coordinates, we propose an efficient model-based gaze tracking system. We first build a unified 3D eye model to relate gaze directions and eye features (pupil center, eyeball center, cornea center) through subject-dependent eye parameters. A personal calibration framework is further proposed to estimate the subject-dependent eye parameters. Finally we can perform real time gaze tracking given the 3D coordinates of eye features from Kinect and the subject-dependent eye parameters from personal calibration procedure. Experimental results with 6 subjects prove the effectiveness of the proposed 3D eye model and the personal calibration framework. Furthermore, the gaze tracking system is able to work in real time (20 fps) and with low resolution eye images.

In face recognition, face rotation alignment is an important part of the recognition process. In this paper, we present a hierarchical detector system using eye and eye-pair detectors combined with a geometrical method for calculating the in-plane angle of a face image. Two feature extraction methods, the restricted Boltzmann machine and the histogram of oriented gradients, are compared to extract feature vectors from a sliding window. Then a support vector machine is used to accurately localize the eyes. After the eye coordinates are obtained through our eye detector, the in-plane angle is estimated by calculating the arc-tangent of horizontal and vertical parts of the distance between left and right eye center points. By using this calculated in-plane angle, the face is subsequently rotationally aligned. We tested our approach on three different face datasets: IMM, Labeled Faces in the Wild (LFW) and FERET. Moreover, to compare the effect of rotational aligning on face recognition performance, we performed experiments using a face recognition method using rotationally aligned and non-aligned face images from the IMM dataset. The results show that our method calculates the in-plane rotation angle with high precision and this leads to a significant gain in face recognition performance.

Locating eyes in face images is an important step for automatic face analysis and recognition. In this paper, we present a novel approach for eye detection without finding the face region using topographic features. First, we argue that the eyes show certain topographic pattern if the gray-level face image is treated as a 3D terrain surface. Then a terrain map, which denotes the terrain type of each pixel, is derived from the original image by applying topographic classification approach. From the terrain map, eyes usually locate in the region around the pit-labeled pixels because of their intrinsic reflectance characteristic. At last, we construct Gaussian mixture model based probabilistic model to describe the distribution of pit-labeled candidates. The eye pair detection problem is transformed to maximize the probability that the selected candidate pair belongs to the eye space. Experiments show that our method has certain robustness to uncontrolled background