Eye Center Localization Research Articles

Feature local binary patterns with application to eye detection

This paper presents a new Feature Local Binary Patterns (FLBP) method that encodes the information of both local texture and features. The features are broadly defined by, for example, the edges, the Gabor wavelet features, the color features, etc. Specifically, a binary image is first derived by extracting feature pixels from a given image, and then a distance vector field is obtained by computing the distance vector between each pixel and its nearest feature pixel defined in the binary image. Based on the distance vector field and the FLBP parameters, the FLBP representation of the given image can be formed. In contrast to the original LBP that only compares a pixel with the pixels in its own neighborhood; the FLBP can compare a pixel with the pixels in its own neighborhood as well as in other neighborhoods. The experimental results on eye detection using the BioID and FERET databases show the feasibility of our FLBP method. In particular, first, the FLBP method significantly improves upon the LBP method in terms of both eye detection rate and eye center localization accuracy. Second, we present a new feature pixel extraction method—the LBP with Relative Bias Thresholding (LRBT) method. The new LRBT method helps improve the FLBP eye detection performance when compared with other feature pixel extraction methods. Third, the FLBP method displays superior representational power and flexibility to the LBP method due to the introduction of the feature pixels as well as the FLBP parameters. Finally, in comparison with some state-of-the-art methods, our FLBP method achieves the highest accuracy of eye center localization.

Accurate Eye Center Location through Invariant Isocentric Patterns

Locating the center of the eyes allows for valuable information to be captured and used in a wide range of applications. Accurate eye center location can be determined using commercial eye-gaze trackers, but additional constraints and expensive hardware make these existing solutions unattractive and impossible to use on standard (i.e., visible wavelength), low-resolution images of eyes. Systems based solely on appearance are proposed in the literature, but their accuracy does not allow us to accurately locate and distinguish eye centers movements in these low-resolution settings. Our aim is to bridge this gap by locating the center of the eye within the area of the pupil on low-resolution images taken from a webcam or a similar device. The proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve in-plane rotational invariance, and to keep low-computational costs. To further gain scale invariance, the approach is applied to a scale space pyramid. In this paper, we extensively test our approach for its robustness to changes in illumination, head pose, scale, occlusion, and eye rotation. We demonstrate that our system can achieve a significant improvement in accuracy over state-of-the-art techniques for eye center location in standard low-resolution imagery.

Precise localization of eye centers with multiple cues

Automatic detection and precise localization of human eye centers are the essential processes in photo related multimedia applications. Since eye center points are used as reference base points for further intelligent processing, precise eye center localization is very important. In face recognition the accuracy of localization of eye centers directly influences the identification accuracy. A multiple stage approach with multiple cues for detection and precise localization of eye centers is presented in this paper. Multiple scopes searching strategy is used for correctly extracting eye patch images from the background. Dedicated gradient based features and curvelet based features are constructed and used for comprehensively revealing the intensity distribution characteristics and the edge based texture around eye centers. A rebuilt score calculation mechanism is proposed and the rebuilt scores are used as a specific measurement index reflecting the matching accuracy. The final localizations of eye centers are determined with integrating the gradient based scores and curvelet based scores. The experiment results testing on public face datasets show that the localization accuracy of proposed approach outperforms the accuracy with other state of the art methods.

PRECISE EYE AND MOUTH LOCALIZATION

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.

Facial feature detection using distance vector fields

A novel method for eye and mouth detection and eye center and mouth corner localization, based on geometrical information is presented in this paper. First, a face detector is applied to detect the facial region, and the edge map of this region is calculated. The distance vector field of the face is extracted by assigning to every facial image pixel a vector pointing to the closest edge pixel. The x and y components of these vectors are used to detect the eyes and mouth regions. Luminance information is used for eye center localization, after removing unwanted effects, such as specular highlights, whereas the hue channel of the lip area is used for the detection of the mouth corners. The proposed method has been tested on the XM2VTS and BioID databases, with very good results.

Estimation of Hurricane Winds From SeaWinds at Ultrahigh Resolution

Although the SeaWinds scatterometer was not specifically designed to observe tropical cyclones, new high-resolution wind products resolve much of the horizontal structure of these storms. However, these higher resolution products (2.5 km) are inherently noisier than the standard 25-km near-surface wind products. These noise levels combined with rain contamination complicate high-resolution wind estimation-particularly in tropical cyclones. Fortunately, tropical cyclones have structures that can be exploited by using a wind field model. This paper develops a new procedure for hurricane wind field estimation from the SeaWinds instrument at ultrahigh resolution. A simplified hurricane model is developed to provide prior information to be used in maximum a posteriori probability estimation of ocean winds. Using the hurricane model ameliorates the effects of rain and noise and provides useful hurricane parameters such as the eye center location. The model also improves ambiguity selection. The new method reduces the variability of the wind speed and direction estimates, although high wind speeds still tend to be underestimated. The method also greatly improves wind direction estimates in hurricanes-even in rain-contaminated portions of the storm.