Zhengzi Wang

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Infrared face recognition, being light-independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. Local binary pattern (LBP), as a classic local feature descriptor, is appreciated for infrared face feature representation. To extract discriminative subset in LBP features,(More)
The research presented in this paper is aimed at the development of an automated imaging system for classification of tissues in medical images obtained from Computed Tomography (CT) scans. The paper focuses on using wavelet-based contourlet packets (WBCP)-based multi-resolution texture analysis. The approach consists of two steps: automatic extraction of(More)
Infrared imaging can acquire the intrinsic temperature information of the skin, which is robust to the impacts of illumination conditions and disguises. This paper proposes an improved infrared face recognition method based on local binary pattern (LBP). To extract the local robust features in infrared face images, LBP is chosen to get the composition of(More)
The compact and discriminative feature extraction is vital for infrared face recognition. This paper proposes a personalized feature selection algorithm for infrared face recognition. Firstly, LBP operator is applied to infrared face for texture information. Secondly, for each subject, a two-class training problem is constructed by one to other means. Then,(More)
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