A statistical quadtree decomposition to improve face analysis
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2016-02-24
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Amaral V.
Giraldi G. A.
Carlos E. Thomaz
Giraldi G. A.
Carlos E. Thomaz
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ICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
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AMARAL, V.; GIRALDI, G. A.; THOMAZ, C. E. ICPRAM 2016 Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods. A statistical quadtree decomposition to improve face analysis, p. 375-380, 2016.
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© Copyright 2016 by SCITEPRESS-Science and Technology Publications, Lda. All rights reserved.The feature extraction is one of the most important steps in face analysis applications and this subject always received attention in the computer vision and pattern recognition areas due to its applicability and wide scope. However, to define the correct spatial relevance of physiognomical features remains a great challenge. It has been proposed recently, with promising results, a statistical spatial mapping technique that highlights the most discriminating facial features using some task driven information from data mining. Such priori information has been employed as a spatial weighted map on Local Binary Pattern (LBP), that uses Chi-Square distance as a nearest neighbour based classifier. Intending to reduce the dimensionality of LBP descriptors and improve the classification rates we propose and implement in this paper two quad-tree image decomposition algorithms to task related spatial map segmentation. The first relies only on split step (top-down) of distinct regions and the second performs the split step followed by a merge step (bottom-up) to combine similar adjacent regions. We carried out the experiments with two distinct face databases and our preliminary results show that the top-down approach achieved similar classification results to standard segmentation using though less regions.