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检索条件"机构=Biometrics and Pattern Recognition Laboratory"

共 104 条记录，以下是61-70 订阅

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Detecting individual in crowd with moving feature's structure consistency

Detecting individual in crowd with moving feature's structur...

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

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

In this paper, we present a method for detecting individuals in crowd by clustering a group of feature points belonging to the same person. In our approach, a feature point is considered to contain three attributes: the motion trajectory in video sequence, the sparse local appearance around point in current frame, and the structure relationship with body center related with local appearance. We exploit these attributes to cluster them appearing on the same individual to achieve detection purpose. The algorithm does not require observing entire human body and could discriminate different individuals under overlap. Our experiments show that this approach advances the performance of detecting individuals in crowds.

关键词： Feature extraction Power capacitors Robustness Trajectory Video sequences Clustering algorithms Humans

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Local frequency descriptor for low-resolution face recognition

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

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International Conference on Automatic Face and Gesture recognition

作者： Zhen Lei Timo Ahonen Matti Pietikäinen Stan Z. Li Center of Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Machine Vision Group University of Oulu Finland

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.

关键词： Face recognition Face Image resolution Probes Robustness Histograms Databases

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Soft biometric classification using periocular region features

Soft biometric classification using periocular region featur...

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IEEE International Conference on biometrics: Theory, Applications, and Systems (BTAS)

作者： Jamie R. Lyle Philip E. Miller Shrinivas J. Pundlik Damon L. Woodard Biometrics and Pattern Recognition Laboratory School of Computing Clemson University Clemson SC USA

With periocular biometrics gaining attention recently, the goal of this paper is to investigate the effectiveness of local appearance features extracted from the periocular region images for soft biométrie classification. We extract gender and ethnicity information from the periocular region images using grayscale pixel intensities and periocular texture computed by Local Binary patterns as our features and a SVM classifier. Results are presented on the visible spectrum periocular images obtained from the FRGC face dataset. For 4232 periocular images of 404 subjects, we obtain a baseline gender and ethnicity classification accuracy of 93% and 91%, respectively, using 5-fold cross validation. Furthermore, we show that fusion of the soft biométrie information obtained from our classification approach with the texture based periocular recognition approach results in an overall performance improvement.

关键词： Face Support vector machines Training Gray-scale Accuracy Pixel Feature extraction

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Performance evaluation of local appearance based periocular recognition

Performance evaluation of local appearance based periocular ...

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IEEE International Conference on biometrics: Theory, Applications, and Systems (BTAS)

作者： Philip E. Miller Jamie R. Lyle Shrinivas J. Pundlik Damon L. Woodard Biometrics and Pattern Recognition Laboratory School of Computing Clemson University Clemson SC USA

The human periocular region is known to be one of the most discriminative regions of a face image, and recent studies have indicated its potential as a biometric trait. However, the bulk of the previous work concerning the periocular region consists of feasibility studies that report recognition results on controlled data, and lacks rigorous performance evaluation, thus leaving various open questions regarding the effectiveness of periocular region as a biométrie modality. In this paper we present a performance evaluation of a local periocular texture based recognition approach. Specifically, the paper investigates the effect of input image quality on recognition performance, the uniqueness of texture between different color channels, and texture information present in different color channels. recognition results of periocular texture features are compared to those of full face texture features and suggest that periocular texture features are robust to varying image quality.

关键词： Face Lighting Image recognition Iris recognition Image color analysis Face recognition

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On the Fusion of Periocular and Iris biometrics in Non-ideal Imagery

On the Fusion of Periocular and Iris Biometrics in Non-ideal...

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International Conference on pattern recognition

作者： Damon L. Woodard Shrinivas Pundlik Philip Miller Raghavender Jillela Arun Ross Biometrics and Pattern Recognition Laboratory School of Computing Clemson University USA Lane Department of Computer Science and Electrical Engineering West Virginia University USA

Human recognition based on the iris biometric is severely impacted when encountering non-ideal images of the eye characterized by occluded irises, motion and spatial blur, poor contrast, and illumination artifacts. This paper discusses the use of the periocular region surrounding the iris, along with the iris texture patterns, in order to improve the overall recognition performance in such images. Periocular texture is extracted from a small, fixed region of the skin surrounding the eye. Experiments on the images extracted from the Near Infra-Red (NIR) face videos of the Multi Biometric Grand Challenge (MBGC) dataset demonstrate that valuable information is contained in the periocular region and it can be fused with the iris texture to improve the overall identification accuracy in non-ideal situations.

关键词： Iris recognition Videos Pixel Skin Databases Face recognition

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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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Transforming traditional iris recognition systems to work on non-ideal situations

Transforming traditional iris recognition systems to work on...

引用

IEEE Workshop on Computational Intelligence in biometrics: Theory, Algorithms, and Applications, CIB

作者： Zhi Zhou Yingzi Du Craig Belcher Biometrics and pattern recognition laboratory in the Electrical and Computer Engineering Indiana University-Purdue University Indianapolis IN USA

Non-ideal iris images can significantly affect the accuracy of iris recognition systems for two reasons: 1) they cannot be properly preprocessed by the system; and/or 2) they have poor image quality. However, many traditional iris recognition systems have been deployed in law enforcement, military, or many other important locations. It will be expensive to replace all these systems. It will be desirable if the traditional systems can be transformed to perform in non-ideal situations without an expensive update. In this paper, we propose a method that can help traditional iris recognition systems to work on the non-ideal situation using a video image approach. The proposed method will quickly identify and eliminate the bad quality images from iris videos for further processing. The segmentation accuracy is critical in recognition and would be challenging for traditional systems. The segmentation evaluation is designed to evaluate if the segmentation is valid. The information distance based quality measure is used to evaluate if the image has enough quality for recognition. The segmentation evaluation score and quality score are combined to predict the recognition performance. The research results show that the proposed methods can work effectively and objectively. The combination of segmentation and quality scores is highly correlated with the recognition accuracy and can be used to improve the performance of iris recognition systems in a non-ideal situation. The deployment of such a system would not cost much since the core parts of the traditional systems are not changed and we only need to add software modules. It will be very practical to transform the traditional system using the proposed method.

关键词： Iris recognition Quantum cellular automata Quantum dots Fault tolerance Coupling circuits Quantum mechanics Electrons Manufacturing Logic testing Costs

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Learning semantic scene models by object classification and trajectory clustering

Learning semantic scene models by object classification and ...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Tianzhu Zhang Hanqing Lu Stan Z. Li National Laboratory of Pattern Recognition Chinese Academy and Sciences Beijing China National Laboratory of Pattern Recognition & Center for Biometrics and Security Research Institute of Automation Chinese Academy and Sciences Beijing China

Activity analysis is a basic task in video surveillance and has become an active research area. However, due to the diversity of moving objects category and their motion patterns, developing robust semantic scene models for activity analysis remains a challenging problem in traffic scenarios. This paper proposes a novel framework to learn semantic scene models. In this framework, the detected moving objects are first classified as pedestrians or vehicles via a co-trained classifier which takes advantage of the multiview information of objects. As a result, the framework can automatically learn motion patterns respectively for pedestrians and vehicles. Then, a graph is proposed to learn and cluster the motion patterns. To this end, trajectory is parameterized and the image is cut into multiple blocks which are taken as the nodes in the graph. Based on the parameters of trajectories, the primary motion patterns in each node (block) are extracted via Gaussian mixture model (GMM), and supplied to this graph. The graph cut algorithm is finally employed to group the motion patterns together, and trajectories are clustered to learn semantic scene models. Experimental results and applications to real world scenes show the validity of our proposed method.

关键词： Layout Vehicles Traffic control pattern recognition pattern analysis Video surveillance Laboratories biometrics National security Automation

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Multi-level iris video image thresholding

Multi-level iris video image thresholding

引用

IEEE Workshop on Computational Intelligence in biometrics: Theory, Algorithms, and Applications, CIB

作者： Yingzi Du N. Luke Thomas Emrah Arslanturk Biometrics and pattern recognition laboratory in the Electrical and Computer Engineering Department Indiana University-Purdue University Indianapolis IN USA

Iris recognition has been shown to be one of the most accurate biometrics. However, under non-ideal situations, its recognition accuracy can be reduced dramatically. Under such situations, video images can be used to improve the accuracy. The traditional single image based segmentation method could be inefficient. Video image based thresholding method can help improve the segmentation efficiency. However, traditional thresholding methods are not designed for iris images. In this paper, the multi-level iris video image thresholding method is proposed. It takes advantage of the correlations between consecutive images for video based thresholding. It is an orientation invariant thresholding scheme. K-mean clustering is used to find the clusters and PCA is used to quickly project the image to the clusters. The experimental results show the proposed method is effective. The thresholded images show clear pupil and iris areas, which can help further iris segmentation and processing. In addition, the proposed method can be applied to non-video images if they are obtained from the same sensor with similar illumination conditions.

关键词： Image segmentation Iris recognition Histograms biometrics Lighting Entropy Focusing Design methodology Principal component analysis Image sensors

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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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International Conference on Document Analysis and recognition

作者： Bangy Li Zhenan Sun Tieniu Tan Center of Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences China

ISBN: (纸本)9781424445004

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.

关键词： Writing Probability distribution pattern recognition biometrics Feature extraction Shape measurement Text analysis pattern analysis Handwriting recognition Sun

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