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Low-resolution face recognition and feature selection based on multidimensional scaling joint L 2,1-norm regularisation

Low-resolution face recognition and feature selection based on multidimensional scaling joint L 2,1-norm regularisation

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Face recognition is confronted with situations wherein the captured face images are not as clear as the registered ones; this is known as low-resolution face recognition. To solve this problem, the authors propose a new sparse coupled projection method using multidimensional scaling joint L 2,1-norm regularisation (MDSL21). The MDSL21 maps the low-resolution faces and high-resolution faces into a common sparse subspace in which feature selection and coupled transformation can be simultaneously achieved. In their proposed method, the authors first learn the common responds using the multidimensional scaling model. Specifically, the distance between the responds is approximated to the distance between the high-resolution samples, and the local manifolds are preserved. Then, the seeking coupled projections are formulated as a regression model. Inspired by the sparse constraint utilised in the classical subspace learning methods, the authors add an L 2,1-norm regularisation term to the regression model to realise the sparsity and present its optimisation method. Experimental results validate the effectiveness of their proposed method on the low-resolution face recognition task.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5044
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