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Semi-supervised uncorrelated dictionary learning for colour face recognition

Semi-supervised uncorrelated dictionary learning for colour face recognition

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Colour images are increasingly used in the fields of computer vision, pattern recognition and machine learning, since they can provide more identifiable information than greyscale images. Thus, colour face recognition has attracted accumulating attention. Its key problem is how to remove the similarity between colour component images and take full advantage of colour difference information. Decision-level similarity reduction between colour component images directly affects the recognition effect, but it has been found in no work. In this study, the authors propose a novel colour face recognition approach named semi-supervised uncorrelated dictionary learning (SUDL), which realises decision-level similarity reduction and fusion of all colour components in face images. SUDL employs the labelled and unlabelled colour face image samples into structured dictionary learning to achieve three uncorrelated discriminating dictionaries corresponding to three colour components of face images, and then uses these dictionaries and the sparse coding technique to make a classification decision. Experimental results in multiple public colour face image databases demonstrate that the dictionary decorrelation, structured dictionary learning and unlabelled samples used in the proposed approach are effective and reasonable, and the proposed approach outperforms several representative colour face recognition methods in recognition rates, despite of its poor time performance.

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