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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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Auxiliary Demographic Information Assisted Age Estimation With Cascaded Structure

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2531-2541页

作者： Wan, Jun Tan, Zichang Lei, Zhen Guo, Guodong Li, Stan Z. Center for Biometrics and Security Research and the National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA

Owing to the variations including both intrinsic and extrinsic factors, age estimation remains a challenging problem. In this paper, five cascaded structure frameworks are proposed for age estimation based on convolutional neural networks. All frameworks are learned and guided by auxiliary demographic information, since other demographic information (i.e., gender and race) is beneficial for age prediction. Each cascaded structure framework is embodied in a parent network and several subnetworks. For example, one of the applied framework is a gender classifier trained by gender information, and then two subnetworks are trained by the male and female samples, respectively. Furthermore, we use the features extracted from the cascaded structure frameworks with Gaussian process regression that can boost the performance further for age estimation. Experimental results on the MORPH II and CACD datasets have gained superior performances compared to the state-of-the-art methods. The mean absolute error is significantly reduced from 3.63 to 2.93 years under the same test protocol on the MORPH II dataset.

关键词： Age estimation convolutional neural networks (CNNs) demographic Gaussian process regression (GPR)

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Hierarchical shape primitive features for online text-independent writer identification

Hierarchical shape primitive features for online text-indepe...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Li, Bangy Sun, Zhenan Tan, Tieniu Center for Biometrics and Security Research National Lab. of Pattern Recognition Chinese Academy of Science China

ISBN: (纸本)9780769537252

This paper proposes a novel method to text-independent writer identification from online handwriting. The main contributions of our method include two parts: shape primitive representation and hierarchical structure. Both shape primitive's features are developed to represent the robust and distinctive characteristics of handwriting in two hierarchies. In first hierarchy, the shape primitives probability distribution function (SPPDF)is defined as the static features, to characterize orientation information of writing style. For each shape primitive, the statistics of pressure is defined as the dynamic shape primitives probability distribution function (DSPPDF) and the second hierarchy we build Gaussian model in dynamic attributes (DA) according to curvature of shape primitives. Experiments were conducted on the NLPR handwriting database collected from 242 persons. The results show that the new method achieves high accuracy, fast speed and low requirement of the amount of characters in handwriting samples. We achieve a writer identification rate of 91.5 % with datasets in Chinese text and 93.6 % in English text. © 2009 IEEE.

关键词： Distribution functions

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Ordinal measures for iris recognition

Ordinal measures for iris recognition

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作者： Sun, Zhenan Tan, Tieniu Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation No. 95 Zhongguancun East Road Beijing 100190 China

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models. © 2009 IEEE.

关键词： biometrics

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Accelerating face recognition for large data applications in visual internet of things

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Information Technology Journal 2013年第6期12卷 1143-1151页

作者： Jiang, Zhuoxuan Lin, Yaping Li, Stan Z. School of Information Science and Engineering Hunan University No. 252 Lushan South Road Changsha 410082 China Center for Biometrics and Security Research National Laboratory of Pattern Recognition Chinese Academy of Sciences 95 Zhongguancun Donglu Beijing 100080 China

This study described a face recognition system for large scale data applications in the framework of Visual Internet of Things (VToT). The main issue here was the speed in large face matching. In order to solve this problem, this study proposed a general method that could accelerate the computation in several magnitudes based on classical k-means clustering while approximately maintaining the original face recognition rate. Experimental results showed that it can speed up existing face recognition systems about 3 times. © 2013 Asian Network for Scientific Information.

关键词： Internet of things

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Counterfeit iris detection based on texture analysis

Counterfeit iris detection based on texture analysis

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作者： Wei, Zhuoshi Qiu, Xianchao Sun, Zhenan Tan, Tieniu Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation P.O. Box 2728 Beijing 100190 China

ISBN: (纸本)9781424421756

This paper addresses the issue of counterfeit iris detection, which is a liveness detection problem in biometrics. Fake iris mentioned here refers to iris wearing color contact lens with textures printed onto them. We propose three measures to detect fake iris: measuring iris edge sharpness, applying Iris-Texton feature for characterizing the visual primitives of iris textures and using selected features based on co-occurrence matrix (CM). Extensive testing is carried out on two datasets containing different types of contact lens with totally 640 fake iris images, which demonstrates that Iris-Texton and CM features are effective and robust in anticounterfeit iris. Detailed comparisons with two stateof- the-art methods are also presented, showing that the proposed iris edge sharpness measure acquires a comparable performance with these two methods, while Iris-Texton and CM features outperform the state-of-the-art. © 2008 IEEE.

关键词： Contact lenses

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Ordinal representations for biometrics recognition

Ordinal representations for biometrics recognition

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15th European Signal Processing Conference, EUSIPCO 2007

作者： Tan, Tieniu Sun, Zhenan Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O. Box 2728 Beijing 100080 China

ISBN: (纸本)9788392134022

biometrics provides a reliable method for automatic personal identification and has wide and important applications. The success of a biometric recognition system depends critically on its feature representation model for biometric patterns. The most challenging issue in biometric feature representation is to achieve sensitivity to inter-class differences and at the same time robustness against intra-class variations. Many biometric representation schemes have been reported but the above issue remains to be resolved. In this paper, we introduce ordinal measures for iris, face and palmprint image representation in an attempt to resolve this issue. With this so-called ordinal representation model in place, many best-performing biometric recognition methods may be interpreted as special cases of this model. In this sense, the proposed ordinal representation model forms a general framework for biometric pattern representation. Extensive experimental results on public biometric databases demonstrate the effectiveness and generality of this representation. © 2007 EURASIP.

关键词： biometrics

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Technology roadmap for smart Iris recognition

Technology roadmap for smart Iris recognition

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18th International Conference on Computer Graphics and Vision, GraphiCon 2008

作者： Sun, Zhenan Dong, Wenbo Tan, Tieniu Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O. Box 2728 Beijing 100190 China

Iris recognition has many desirable properties for reliable individual authentication but usability is its largest bottleneck to wide deployment. Thus smart interface and machine intelligent are the objective of next-generation iris recognition. This paper presents the technology roadmap for smart iris recognition (SIR). Firstly, the concept of SIR is introduced, including its definition, characteristics and performance target. Then the evolution process of iris acquisition and recognition algorithm is proposed respectively. With various strategies of human-machine interaction, iris acquisition systems are grouped into seven categories, i.e. Close-range IR, Active IR, IR at a distance, Active IR at a distance, Passive IR on move, Active IR on move, IR for Surveillance. Iris recognition algorithms advance to be more accurate, robust, efficient and secure. The achievements of state-of-the-art iris recognition methods especially the contributions of our research group are reviewed in the roadmap.

关键词： biometrics

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Synthesis of large realistic iris databases using patch-based sampling

Synthesis of large realistic iris databases using patch-base...

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作者： Wei, Zhuoshi Tan, Tieniu Sun, Zhenan Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation P.O. Box 2728 Beijing 100190 China

ISBN: (纸本)9781424421756

This paper presents a framework to synthesize large realistic iris databases, providing an alternative to iris database collection. Firstly, iris patch is used as a basic element to characterize visual primitive of iris texture, and patch-based sampling is applied to create an iris prototype. Then a set of pseudo irises with intra-class variations are derived from the prototype. Qualitative and quantitative studies reveal that synthetic databases are well suited for evaluating iris recognition systems by achieving three goals: (1) the synthetic iris images bear a close resemblance to real iris images in terms of visual appearance;(2) the proposed framework is able to generate databases with large capacity;(3) statistical performance shows that the synthetic iris images hold all the major characteristics of real iris images. © 2008 IEEE.

关键词： Database systems

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