IEEE Trans Image Process - Regularized discriminative spectral regression method for heterogeneous face matching.

Tópicos

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Resumo

Face recognition is confronted with situations in which face images are captured in various modalities, such as the visual modality, the near infrared modality, and the sketch modality. This is known as heterogeneous face recognition. To solve this problem, we propose a new method called discriminative spectral regression (DSR). The DSR maps heterogeneous face images into a common discriminative subspace in which robust classification can be achieved. In the proposed method, the subspace learning problem is transformed into a least squares problem. Different mappings should map heterogeneous images from the same class close to each other, while images from different classes should be separated as far as possible. To realize this, we introduce two novel regularization terms, which reflect the category relationships among data, into the least squares approach. Experiments conducted on two heterogeneous face databases validate the superiority of the proposed method over the previous methods.

Resumo Limpo

face recognit confront situat face imag captur various modal visual modal near infrar modal sketch modal known heterogen face recognit solv problem propos new method call discrimin spectral regress dsr dsr map heterogen face imag common discrimin subspac robust classif can achiev propos method subspac learn problem transform least squar problem differ map map heterogen imag class close imag differ class separ far possibl realiz introduc two novel regular term reflect categori relationship among data least squar approach experi conduct two heterogen face databas valid superior propos method previous method

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