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Discriminative and Compact Coding for Robust Face Recognition
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DOI：10.1109/TCYB.2014.2361770.png)
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Abstract
En 中文
In this paper, we propose a novel discriminative and compact coding (DCC) for robust face recognition. It introduces multiple error measurements into regression model. They collaborate to tune regression codes of different properties (sparsity, compactness, high discriminating ability, etc.), to further improve robustness and adaptivity of the regression model. We propose two types of coding models: 1) multiscale error measurements that produces sparse and highly discriminative codes and 2) inspires within-class collaborative representation that produces sparse and compact codes. The update of codes and the combination of different errors are automatically processed. DCC is also robust to the choice of parameters, producing stable regression residuals which are crucial to classification. Extensive experiments on benchmark datasets show that DCC has promising performance and outperforms other state-of-the-art regression models.
Keywords:
Compactness
discriminative and compact coding (DCC)
multiple error measurements
robust face recognition
sparsity
within-class collaborative representation
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