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Reflection-type Finger Vein Recognition for Mobile Applications
Congcong Zhang,Zhi Liu,Yi Liu,Fangqi Su,Jun Chang,Yiran Zhou,Qijun Zhao 한국광학회 2015 Current Optics and Photonics Vol.19 No.5
Finger vein recognition, which is a promising biometric method for identity authentication, has attractedsignificant attention. Considerable research focuses on transmission-type finger vein recognition, but thistype of authentication is difficult to implement in mobile consumer devices. Therefore, reflection-type fingervein recognition should be developed. In the reflection-type vein recognition field, the majority ofresearchers concentrate on palm and palm dorsa patterns, and only a few pay attention to reflection-typefinger vein recognition. Thus, this paper presents reflection-type finger vein recognition for biometricapplication that can be integrated into mobile consumer devices. A database is built to test the proposedalgorithm. A novel method of region-of-interest localization for a finger vein image is introduced, anda scheme for effectively extracting finger vein features is proposed. Experiments demonstrate the feasibilityof reflection-type finger vein recognition
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Hyperspectral Image Unmixing for Classification and Recognition : An Overview
Mingyu Nie,Zhi Liu,Hui Xu,Xiaoyan Xiao,Fangqi Su,Jun Chang,Xiaomei Li 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.12
The limited resolution of image sensors and the complex diversity of nature, cause mixed pixel problems in hyperspectral technology. Such problems are common, and increase the complexity of hyperspectral image processing. Hyperspectral unmixing is crucial for hyperspectral image classification and recognition. In unmixing, the image signatures are represented as a linear combination of the basic materials. Unmixing is the process of decomposing a mixed pixel into constituent materials, and calculating the corresponding fractional abundance. If pure materials (end members) are present in an image, unmixing can be divided into two steps, namely, end member extraction and abundance decomposition. On the other hand, if there is no pure material, researchers have devised and investigated unsupervised and semi-supervised spectral unmixing technology. This article presents an overview of the state-of-the-art methods of hyperspectral unmixing and their extensions.
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