Fractals for the classroom (vol. 1): strategic activities
Fractals for the classroom (vol. 1): strategic activities
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractional Box-Counting Approach to Fractal Dimension Estimation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Deformable templates for face recognition
Journal of Cognitive Neuroscience
Human face recognition based on spatially weighted Hausdorff distance
Pattern Recognition Letters
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In this paper, an efticient automatic human face recognition system is proposed. Fractal dimension is an efficient representation of texture which is used to locate the eyes in a human face. We propose a modified approach to estimate the fractal dimensions which is less sensitive to lighting conditions and provides information about the orientation of an image under consideration. Based on the position of the eyes, two face images are normalized, aligned and then compared by a new modified Hausdorff distance measure. As different facial regions have different degrees of importance for face recognition, the modified Hausdorff distance is weighted according to a weighted function derived from the spatial information of the human face. Experimental results show that our approach can achieve recognition rates of 76%, 84%, and 92% for the first one, the first five, first ten likely matched faces, respectively. If the position of the eyes is selected manually, the corresponding recognition rates are 82%, 95% and 98%, respectively. The average processing time for detecting the eyes and recognize a human face is less than two seconds.