LPT: Eye Features Localizer in an N-Dimensional Image Space

Facial feature extraction is one of the most important challenges in the area of facial image processing. This paper introduces a new method for locating eye features that is capable of processing images rapidly while achieving high detection rates. The proposed method is applicable to an n-dimensional space. Therefore, a new representation is considered for image, where an m×n image consists of m observation sets in an n-dimensional space. The main contribution to this paper is proposing a one-to-one linear transform based on this new representation called Linear Principal Transformation (LPT). LPT reduces the dimension of the image from n to two and allows extraction of all image features rapidly and efficiently. A set of experiments on the FERET and IFDB image data set is presented. The performance of eye feature extraction system is comparable to the best previous systems, where the success rate of the proposed method is 95.2%.

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