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The CASIA NIR-VIS 2.0 Face Database

The CASIA NIR-VIS 2.0 Face Database

引用

IEEE Computer Society Conference on Computer Vision and pattern recognition Workshops (CVPRW)

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

In recent years, heterogeneous face biometrics has attracted more attentions in the face recognition community. After published in 2009, the HFB database has been applied by tens of research groups and widely used for Near infrared vs. Visible light (NIR-VIS) face recognition. Despite its success the HFB database has two disadvantages: a limited number of subjects, lacking specific evaluation protocols. To address these issues we collected the NIR-VIS 2.0 database. It contains 725 subjects, imaged by VIS and NIR cameras in four recording sessions. Because the 3D modality in the HFB database was less used in the literature, we don't consider it in the current version. In this paper, we describe the composition of the database, evaluation protocols and present the baseline performance of PCA on the database. Moreover, two interesting tricks, the facial symmetry and heterogeneous component analysis (HCA) are also introduced to improve the performance.

关键词： Databases Face Principal component analysis Face recognition Protocols Training Feature extraction

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Person-specific face tracking with online recognition

Person-specific face tracking with online recognition

引用

International Conference on Automatic Face and Gesture recognition

作者： Zhaowei Cai Longyin Wen Dong Cao 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 China

ISBN: (纸本)9781467355452

Person-specific face tracking is a challenging task for the trackers which only focus on the appearance of the target face, because distraction always happens and the identity is difficult to maintain. In this paper, we design a framework combining an off-line detector, an on-line tracker and an online recognizer to complete the tracking of person-specific face. Recognizer is the key component in our framework, because the most confident target face will be selected by the recognizer from the pool of detected and tracked faces. Since there is no much prior information about the identities available and the face poses change frequently in surveillance scenarios, accurate recognition is extremely difficult and an on-line formulation is required. In order to ensure the precision of identity recognition with different poses, we project the extracted features of faces to a latent space with the help of Canonical Correlation Analysis (CCA) technique, and then these projected features are incrementally trained using an on-line SVM (LASVM). Experimental results demonstrate that our person-specific face tracking outperforms several state-of-the-art face trackers.

关键词： Face Target tracking Face recognition Detectors Target recognition Robustness Vectors

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Structural models for face detection

Structural models for face detection

引用

International Conference on Automatic Face and Gesture recognition

作者： Junjie Yan Xucong Zhang 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 China

ISBN: (纸本)9781467355452

Despite the success in the last two decades, the state-of-the-art face detectors still have problems in dealing with images in the wild for the large appearance variations. Instead of taking appearance variations as black boxes and leaving them to statistical learning algorithms, we propose a structural face model to explicitly represent them. Our hierarchical part based structural face model enables part subtype option to describe appearance variations of the local part, and part deformation to capture the deformable variations between different poses and expressions. In the process of detection, the input candidate is first fitted by the structural model to infer the part location and part subtype, and the confidence score is then computed based on the fitted configuration to reduce the influence of structure variation. Besides the face model, we utilize the co-occurrence of face and body to further boost the face detection performance. We present a method for training phrase based body detectors, and propose a structural context model to jointly use the results of face detector and various body detectors. Experiments on the challenging FDDB show that our method has state-of-the-art performance, compared with other commercial and academic systems.

关键词： Face Detectors Face detection Context modeling Deformable models Training Context

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Discriminant image filter learning for face recognition with local binary pattern like representation

Discriminant image filter learning for face recognition with...

引用

Conference on Computer Vision and pattern recognition (CVPR)

作者： 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

Local binary pattern (LBP) and its variants are effective descriptors for face recognition. The traditional LBP like features are extracted based on the original pixel or patch values of images. In this paper, we propose to learn the discriminative image filter to improve the discriminant power of the LBP like feature. The basic idea is after the image filtering with the learned filter, the difference of pixel difference vectors (PDVs) between the images from the same person is consistent and the difference between the images from different persons is enlarged. In this way, the LBP like features extracted from the filtered images are considered to be more discriminant than those extracted from the original images. Moreover, a coupled discriminant image filters learning method is proposed to deal with the heterogenous face images matching problem by reducing the feature gap between the heterogeneous images. Experiments on FERET, FRGC and a VIS-NIR heterogeneous face databases validate the effectiveness of our proposed image filter learning method combined with LBP like features.

关键词： Face Feature extraction Face recognition Vectors Databases Learning systems Training

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Online content-aware video condensation

Online content-aware video condensation

引用

Conference on Computer Vision and pattern recognition (CVPR)

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

Explosive growth of surveillance video data presents formidable challenges to its browsing, retrieval and storage. Video synopsis, an innovation proposed by Peleg and his colleagues, is aimed for fast browsing by shortening the video into a synopsis while keeping activities in video captured by a camera. However, the current techniques are offline methods requiring that all the video data be ready for the processing, and are expensive in time and space. In this paper, we propose an online and efficient solution, and its supporting algorithms to overcome the problems. The method adopts an online content-aware approach in a step-wise manner, hence applicable to endless video, with less computational cost. Moreover, we propose a novel tracking method, called sticky tracking, to achieve high-quality visualization. The system can achieve a faster-than-real-time speed with a multi-core CPU implementation. The advantages are demonstrated by extensive experiments with a wide variety of videos. The proposed solution and algorithms could be integrated with surveillance cameras, and impact the way that surveillance videos are recorded.

关键词： Electron tubes Streaming media Surveillance Optimization Games Target tracking Yarn

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Multi-pedestrian detection in crowded scenes: A global view

Multi-pedestrian detection in crowded scenes: A global view

引用

Conference on Computer Vision and pattern recognition (CVPR)

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 China

Recent state-of-the-art algorithms have achieved good performance on normal pedestrian detection tasks. However, pedestrian detection in crowded scenes is still challenging due to the significant appearance variation caused by heavy occlusions and complex spatial interactions. In this paper we propose a unified probabilistic framework to globally describe multiple pedestrians in crowded scenes in terms of appearance and spatial interaction. We utilize a mixture model, where every pedestrian is assumed in a special subclass and described by the sub-model. Scores of pedestrian parts are used to represent appearance and quadratic kernel is used to represent relative spatial interaction. For efficient inference, multi-pedestrian detection is modeled as a MAP problem and we utilize greedy algorithm to get an approximation. For discriminative parameter learning, we formulate it as a learning to rank problem, and propose Latent Rank SVM for learning from weakly labeled data. Experiments on various databases validate the effectiveness of the proposed approach.

关键词： Training Databases Vectors Data models Probabilistic logic Support vector machines Optimization

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Quality Fusion Based Multimodal Eye recognition

Quality Fusion Based Multimodal Eye Recognition

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Zhi Zhou Eliza Yingzi Du Craig Belcher N. Luke Thomas Edward J. Delp Biometrics and Pattern Recognition Laboratory in the Electrical and Computer Engineering Department Indiana University-Purdue University Electrical and Computer Engineering Department Purdue University

ISBN: (纸本)9781467317139

Multimodal eye recognition can improve the bio metric systems recognition accuracy by combining iris and sclera recognition. However, poor quality images can significantly affect the system performance. In this paper, we proposed a quality fusion based multimodal eye recognition. Our quality measure evaluated the entire eye image quality, iris area quality, and sclera area quality. The experimental results show that our overall iris and sclera quality scores are highly correlated to recognition accuracy, and our quality fusion based eye recognition can improve and predict the performance of eye recognition systems.

关键词： Eye recognition Quality measure Iris recognition Sclera recognition Quality fusion recognition (Psychology) Eye Iris System performance Sclera Iris Image quality Fusion reactions Fusion, biological FUSION fusion (melting)

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Heterogeneous face image matching using multi-scale features

Heterogeneous face image matching using multi-scale features

引用

IAPR International Conference on biometrics (ICB)

作者： Sifei Liu 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 China Research and Development Center for Internet of Thing

Heterogeneous Face recognition (HFR) refers recognition of face images captured in different modalities, e.g. Visual (VIS), near infrared (NIR) and thermal infrared (TIR). Although heterogeneous face images of a given person differ by pixel values, the identity of the face should be classified as the same. This paper focuses on NIR-VIS HFR. Light Source Invariant Features (LSIFs) are derived to extract the invariant parts between two types of face images. The derived LSIFs rely only on the variation patterns of the skin parameters so that the effects generated from light source can be largely reduced. A common feature extraction method is designed to capture LSIFs based on a group of differential-based band-pass image filters, and we show that the scale for filters is critical. Our results in CASIA HFB database validate the effectiveness of the model and our recognition approach.

关键词： Face Light sources Feature extraction Boosting Face recognition Training Databases

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An improved coupled spectral regression for heterogeneous face recognition

An improved coupled spectral regression for heterogeneous fa...

引用

IAPR International Conference on biometrics (ICB)

作者： Zhen Lei Changtao Zhou Dong Yi Anil K. Jain Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Chinese Academy of Sciences Beijing China Michigan State University East Lansing MI U.S.A.

Coupled spectral regression (CSR) is an effective framework for heterogeneous face recognition (e.g., visual light (VIS) vs. near infrared (NIR)). CSR aims to learn different projections for different face modalities respectively to find a common subspace where the samples of different modalities from the same class are as close as possible. In original CSR, the projection for one modality is supposed to be represented by the data from the same modality. In this paper, we show that not only the samples of the same modality, but also all samples from different modalities are useful to learn the projection. Based on this assumption, we propose an improved coupled spectral regression (ICSR) approach which assumes the projections are linearly represented by all samples. Moreover, in order to improve the generalization capability, the locality information among samples is considered during the ICSR learning. Experiments on PIE, Multi-PIE and CASIA-HFB face database show that the proposed ICSR enhances the heterogeneous face recognition performance compared with the original CSR and validates the effectiveness of the proposed method.

关键词： Image resolution Face Face recognition Databases Probes Training Kernel

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Histograms of Gabor Ordinal Measures for face representation and recognition

Histograms of Gabor Ordinal Measures for face representation...

引用

IAPR International Conference on biometrics (ICB)

作者： Zhenhua Chai Ran He Zhenan Sun Tieniu Tan Heydi Méndez-Vázquez Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China Advanced Technologies Application Center CENATAV Havana Cuba

This paper proposes a new image representation method named Histograms of Gabor Ordinal Measures (HOGOM) for robust face recognition. First, a novel texture descriptor, Gabor Ordinal Measures (GOM), is developed to inherit the advantages from Gabor features and Ordinal Measures. GOM applies Gabor filters of different orientations and scales on the face image and then computes Ordinal Measures over each Gabor magnitude response. Second, in order to obtain an effective and compact representation, the binary values of each GOM, for different orientations at a given scale, are encoded into a single decimal number and then spatial histograms of non-overlapping rectangular regions are computed. Finally, a nearest-neighbor classifier with the χ 2 dissimilarity measure is used for classification. HOGOM has three principal advantages: 1) it succeeds the spatial locality and orientation selectivity from Gabor features; 2) the adopted region-comparison strategy makes it more robust; 3) by applying the binary codification and computing spatial histograms, it becomes more stable and efficient. Extensive experiments on the large-scale FERET database and AR database show the robustness of the proposed descriptor, achieving the state of the art.

关键词： Face Histograms Face recognition Databases Robustness Training Statistical learning

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