IEEE Trans Image Process - Very low resolution face recognition problem.

Tópicos

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Resumo

This paper addresses the very low resolution (VLR) problem in face recognition in which the resolution of the face image to be recognized is lower than 16 ? 16. With the increasing demand of surveillance camera-based applications, the VLR problem happens in many face application systems. Existing face recognition algorithms are not able to give satisfactory performance on the VLR face image. While face super-resolution (SR) methods can be employed to enhance the resolution of the images, the existing learning-based face SR methods do not perform well on such a VLR face image. To overcome this problem, this paper proposes a novel approach to learn the relationship between the high-resolution image space and the VLR image space for face SR. Based on this new approach, two constraints, namely, new data and discriminative constraints, are designed for good visuality and face recognition applications under the VLR problem, respectively. Experimental results show that the proposed SR algorithm based on relationship learning outperforms the existing algorithms in public face databases.

Resumo Limpo

paper address low resolut vlr problem face recognit resolut face imag recogn lower increas demand surveil camerabas applic vlr problem happen mani face applic system exist face recognit algorithm abl give satisfactori perform vlr face imag face superresolut sr method can employ enhanc resolut imag exist learningbas face sr method perform well vlr face imag overcom problem paper propos novel approach learn relationship highresolut imag space vlr imag space face sr base new approach two constraint name new data discrimin constraint design good visual face recognit applic vlr problem respect experiment result show propos sr algorithm base relationship learn outperform exist algorithm public face databas

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