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This paper presents a novel manifold learning method, namely Directional two-dimensional neighborhood preserving embedding (Dir-2DNPE), for feature extraction. In contrast to standard NPE, Dir-2DNPE directly seeks the optimal projective vectors from the directional images without image-to-vector transformation. Moreover, Dir-2DNPE can well reserve the spatial correlations between variations of rows...
In this paper, we propose a face detection framework that combines both feature, and skin pixel approaches, while making the framework self adaptive which is important for non controlled environmental conditions. The framework uses skin color information to reduce the search space for faces by localizing the probable skin regions using a mixture of multivariate Gaussians whose parameters are first...
Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this paper, we take a look at two different approaches for eye detection...
This paper presents a robust and precise eye detection algorithm on gray intensity face images. Our method combines the strength of two existing methods which are a feature based method and an appearance based method to detect and locate a precise pupil center. It includes the following three steps. First, the feature based method is used. The method uses a projection function to detect all possible...
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