Abstract
Biometric technique becomes essential to identify individuals in different applications. Face sketch is one of the biometric methods, which are used to identify criminals. In this paper, a face sketch synthesis and recognition model is proposed. In this model, the photo images are transformed to pseudo-sketch images using linear regression technique. Moreover, Gabor filters are used to extract the features from three scales of the images. For each scale, a face sketch image is matched with face pseudo-sketches instead of the original photos to identify an unknown individual. Minimum distance classifier is used to match the sketches with pseudo-sketches in each scale. Further, a classification level fusion is used to combine the outputs of the classifiers at three scales namely, decision, rank, and score level fusion. CHUK database images is used in our experiments. The experimental results show that the proposed model is superior to other existed models in terms of identification accuracy. Moreover, matching sketch images with pseudo-sketches achieved accuracy better than matching sketch images with the original photo images. The proposed model achieved a recognition rate ranged from 82.95 to 94.32 % using single scale, while the accuracy increased to 94.32, 96.6 and 97.7 % when the decision, rank, and score level fusion, respectively, are used.
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Tharwat, A., Mahdi, H., Hennawy, A.E., Hassanien, A.E. (2016). Face Sketch Synthesis and Recognition Based on Linear Regression Transformation and Multi-Classifier Technique. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_17
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DOI: https://doi.org/10.1007/978-3-319-26690-9_17
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