Subclass representation‐based face‐recognition algorithm derived from the structure scatter of training samples
Representation‐based face‐recognition techniques have received attention in the field of pattern recognition in recent years; however, the well‐known works focus mainly on constraint conditions and dictionary learning. Few researchers study, which sample data features determine the performance of re...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-09-01
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| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2015.0350 |
| Summary: | Representation‐based face‐recognition techniques have received attention in the field of pattern recognition in recent years; however, the well‐known works focus mainly on constraint conditions and dictionary learning. Few researchers study, which sample data features determine the performance of representation‐based classification algorithms. To address this problem, the authors define the structure‐scatter degree, which represents the structural features of training sample sets, to determine whether a set is suitable for the representation‐based classification algorithm. Experimental results show that sets with a higher structure scatter more likely allows a classification algorithm to obtain a higher recognition rate. Further, the block contribution degree (DBC) of a training sample set is defined to evaluate whether a sample set is suitable for block‐based sparse‐representation classification algorithms. Experimental results indicate that if the DBC approaches zero, the block technique is unlikely to improve the performance of a representation‐based classification algorithm. Thus, they devise a self‐adaptive optimisation method to generate an optimal block size, an overlapping degree, and a block‐weighting scheme. Finally, they propose the structure scatter‐based subclass representation classification. Experimental results demonstrate that the proposed algorithm not only improves the recognition accuracy of the representation‐based classification algorithm, but also greatly reduces its time complexity. |
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| ISSN: | 1751-9632 1751-9640 |