Overlapping on partitioned facial images

Authors:
Önsen Toygar;Adnan Acan
Affiliations:
Computer Engineering Department, Eastern Mediterranean University, Gazimagusa, Turkey;Computer Engineering Department, Eastern Mediterranean University, Gazimagusa, Turkey
Venue:
SSIP'06 Proceedings of the 6th WSEAS International Conference on Signal, Speech and Image Processing
Year:
2006

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Abstract

The effect of overlapped classifiers on partitioning-based face recognition is presented. The features of facial images are extracted by appearance-based statistical dimensionality reduction algorithms for the recognition of horizontally and vertically partitioned facial images. The proposed approaches employ a divide-and-conquer strategy which aims to improve the recognition performance of holistic methods by emphasizing locally important features over horizontal or vertical segments. Additionally, computational complexity is also reduced significantly since feature extractions are performed over smaller facial regions. Analysis of the obtained results demonstrate that both vertical and horizontal partitioning achieve better recognition performance compared to the holistic counterparts. It is also observed that, for some of the statistical methods, overlapped feature extraction results in better recognition performance compared to disjoint partitioning approach. The proposed implementations achieved particularly superior performance for LDA- and ICA-based classifiers, for which the proposed approaches demonstrated the best so far published results.