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IAPR International Conference on biometrics (ICB)

作者： Jianfei Zhu Dong Cao Sifei Liu Zhen Lei Stan Z. Li Institute of Intelligent Information Processing Xidian University Xi' an China Hohai University Nanjing Jiangsu Province China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Chinese Academy of Sciences Institute of Automation Chinese Academy of Sciences Beijing Beijing CN

This paper presents an occlusion robust image representation method and apply it to face recognition. Inspired from the recent work [15], we propose a Gabor phase difference representation for occlusion robust face recognition. Based on the good ability of Gabor filters to capture image structure and the robustness to image occlusion shown in this paper, Gabor phase features are expected to be discriminative and robust for face representation in occlusion case. Besides, we adopt spectral regression based discriminant analysis with the extracted Gabor phase features to find the most discriminant subspace to classify different faces. In this way, an occlusion robust face image discriminant subspace is derived. Extensive experiments with various occlusion cases show the efficacy of the proposed method.

关键词： Face Robustness Face recognition Databases Kernel Training Principal component analysis

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Face liveness detection by learning multispectral reflectance distributions

Face liveness detection by learning multispectral reflectanc...

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IEEE International Conference on Automatic Face and Gesture recognition

作者： Zhang, Zhiwei Yi, Dong Lei, Zhen Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation China

ISBN: (纸本)9781424491407

Existing face liveness detection algorithms adopt behavioural challenge-response methods that require user cooperation. To be verified live, users are expected to obey some user unfriendly requirement. In this paper, we present a multispectral face liveness detection method, which is user cooperation free. Moreover, the system is adaptive to various user-system distances. Using the Lambertian model, we analyze multispectral properties of human skin versus non-skin, and the discriminative wavelengths are then chosen. Reflectance data of genuine and fake faces at multi-distances are selected to form a training set. An SVM classifier is trained to learn the multispectral distribution for a final Genuine-or-Fake classification. Compared with previous works, the proposed method has the following advantages: (a) The requirement on the users' cooperation is no longer needed, making the liveness detection user friendly and fast. (b) The system can work without restricted distance requirement from the target being analyzed. Experiments are conducted on genuine versus planar face data, and genuine versus mask face data. Furthermore a comparison with the visible challenge-response liveness detection method is also given. The experimental results clearly demonstrate the superiority of our method over previous systems. © 2011 IEEE.

关键词： Reflection

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Learning sparse feature for eyeglasses problem in face recognition

Learning sparse feature for eyeglasses problem in face recog...

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IEEE International Conference on Automatic Face and Gesture recognition

作者： Yi, Dong Li, Stan Z. Center for Biometrics Security Research National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781424491407

Occlusion of eyeglasses, and strong specular reflections on eyeglasses (especially in near infrared (NIR) images), can deteriorate face recognition performance. In this paper, we present a novel method to overcome these problems. The proposed method applies the sparse representation (SR) technique in a local feature space so as to be more tolerant to mis-alignment and abnormal specular pixel values. The SR face features are further transformed by using discriminant analysis. These lead to a good balance between efficiency and robustness. Extensive experiments on a large NIR face database containing 292 persons with/without eyeglasses show the superiority of the proposed method compared with state-of-the-art methods. © 2011 IEEE.

关键词： Eyeglasses

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Review of recent patents on cancelable biometrics

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Recent Patents on Electrical Engineering 2011年第2期4卷 125-132页

作者： Yang, Kai Du, Eliza Yingzi Pattern Recognition and Biometrics Laboratory Department of Electrical and Computer Engineering Indiana University Purdue University Indianapolis 723 W. Michigan Street SL 160 Indianapolis IN 46202 United States

In general, the biometric patterns of an individual are permanently bound with him/her and it is almost impossible to replace. As a result, once stolen or compromised, the biometric data are either lost forever or are very hard to recover. Thus, it is desirable to secure the original patterns and design a method to ensure their revocability when compromised, which is known as "cancelable biometrics". In this paper, recent patents and related papers on cancelable biometrics are reviewed, including different biometrics encryption algorithms and feature transformation methods. © 2011 Bentham Science Publishers.

关键词： biometrics

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Local frequency descriptor for low-resolution face recognition

Local frequency descriptor for low-resolution face recogniti...

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IEEE International Conference on Automatic Face and Gesture recognition

作者： Lei, Zhen Ahonen, Timo Pietikainen, Matti Li, Stan Z. Center for Biometrics and Security Research National Laboratory of Pattern Recognition Chinese Academy of Sciences 95 Zhongguancun Donglu Beijing 100190 China Machine Vision Group University of Oulu FI-90014 Oulun yliopisto Finland

ISBN: (纸本)9781424491407

Face recognition from low-resolution images is a common yet challenging case in real applications. Since the high-frequency information is lost in low-resolution images, it is necessary to explore robust information in the low frequency domain. In this paper, we propose an effective local frequency descriptor (LFD) for low resolution face recognition, by building upon the ideas behind local phase quantization (LPQ) and exploring both blur-invariant magnitude and phase information in the low frequency domain. The proposed descriptor is more descriptive than LPQ with more comprehensive information. In addition, a statistical uniform pattern definition method is introduced to improve the efficiency of the proposed descriptor. Experimental results on FERET and a real video database show that LFD is effective and robust for low-resolution face recognition. © 2011 IEEE.

关键词： Face recognition

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Face liveness detection by learning multispectral reflectance distributions

Face liveness detection by learning multispectral reflectanc...

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International Conference on Automatic Face and Gesture recognition

作者： Zhiwei Zhang Dong Yi Zhen Lei Stan Z. Li Center of Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China

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Learning sparse feature for eyeglasses problem in face recognition

Learning sparse feature for eyeglasses problem in face recog...

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International Conference on Automatic Face and Gesture recognition

作者： Dong Yi Stan Z. Li Center of Biometrics Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

关键词： Face Face recognition Strontium Pixel Probes Databases Robustness

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Towards incremental and large scale face recognition

Towards incremental and large scale face recognition

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IEEE International Joint Conference on biometrics (IJCB)

作者： Junjie Yan Zhen Lei Dong Yi Stan Z. Li Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Linear discriminant analysis with nearest neighborhood classifier (LDA + NN) has been commonly used in face recognition, but it often confronts with two problems in real applications: (1) it cannot incrementally deal with the in- formation of training instances; (2) it cannot achieve fast search against large scale gallery set. In this paper, we use incremental LDA (ILDA) and hashing based search method to deal with these two problems. Firstly two incremental LDA algorithms are proposed under spectral regression framework, namely exact incremental spectral regression discriminant analysis (EI-SRDA) and approximate incremental spectral regression discriminant analysis (AI- SRDA). Secondly we propose a similarity hashing algorithm of sub-linear complexity to achieve quick recognition from large gallery set. Experiments on FRGC and self-collected 100,000 faces database show the effective of our methods.

关键词： Artificial neural networks Face Manganese

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Low-resolution face recognition via Simultaneous Discriminant Analysis

Low-resolution face recognition via Simultaneous Discriminan...

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IEEE International Joint Conference on biometrics (IJCB)

作者： Changtao Zhou Zhiwei Zhang Dong Yi Zhen Lei Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Low resolution (LR) is an important issue when handling real world face recognition problems. The performance of traditional recognition algorithms will drop drastically due to the loss of facial texture information in original high resolution (HR) images. To address this problem, in this paper we propose an effective approach named Simultaneous Discriminant Analysis (SDA). SDA learns two map- pings from LR and HR images respectively to a common subspace where discrimination property is maximized. In SDA, (1) the data gap between LR and HR is reduced by mapping into a common space; and (2) the mapping is de- signed for preserving most discriminative information. After that, the conventional classification method is applied in the common space for final decision. Extensive experiments are conducted on both FERET and Multi-PIE, and the results clearly show the superiority of the proposed SDA over state-of-the-art methods.

关键词： Image resolution Databases

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A robust eye localization method for low quality face images

A robust eye localization method for low quality face images

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IEEE International Joint Conference on biometrics (IJCB)

作者： Dong Yi Zhen Lei Stan Z. Li Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

Eye localization is an important part in face recognition system, because its precision closely affects the performance of face recognition. Although various methods have already achieved high precision on the face images with high quality, their precision will drop on low quality images. In this paper, we propose a robust eye localization method for low quality face images to improve the eye detection rate and localization precision. First, we propose a probabilistic cascade (P-Cascade) framework, in which we reformulate the traditional cascade classifier in a probabilistic way. The P-Cascade can give chance to each image patch contributing to the final result, regardless the patch is accepted or rejected by the cascade. Second, we propose two extensions to further improve the robustness and precision in the P-Cascade framework. There are: (1) extending feature set, and (2) stacking two classifiers in multiple scales. Extensive experiments on JAFFE, BioID, LFW and a self-collected video surveillance database show that our method is comparable to state-of-the-art methods on high quality images and can work well on low quality images. This work supplies a solid base for face recognition applications under unconstrained or surveillance environments.

关键词： Computational efficiency

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