Facial Feature Extraction using Gradient Features and MQDF Matching

W. Ohyama (Japan), M. Shridhar, and P. Watta (USA)


Computer Vision, Facial Feature Extraction, Face Detec tion, Gradient Feature, MQDF–based distance


In this paper, we propose a novel approach for the extrac tion of facial features from frontal face images. The pro posed approach involves 3 main processing stages. In the first stage, a bounding box is placed over the face. In the second stage, candidate positions for the facial features are identified using a set of MQDF-based distance functions. Finally, in the third stage, the candidate feature positions are evaluated using an MQDF-based verification function, and an optimal configuration is chosen. Results on the FERET database indicate that with a proper setting of pa rameters, the proposed algorithm can accurately extract fa cial features over many different scales.

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