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International Conference on pattern recognition

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

ISBN: (纸本)9781479952106

3D Morph able Model (3DMM) has been widely used in face analysis for many years. The most challenging part of 3DMM is to find the correspondences between 3D points and 2D pixels. Existing methods only use key points, edges, specular highlights and image pixels to complete the task, which are not accurate or robust. This paper proposes a new algorithm called Sparse SIFT Flow (SSF) to improve the reconstruction accuracy. We mark a set of salient points to control the shape of facial components and use SSF to find their corresponding pixels on the input image. We also incorporate SSF into Multi-Features Framework to construct a robust 3DMM fitting algorithm. Compared with the state-of-the art, our approach significantly improves the fitting results in facial component area.

关键词： Face Shape Three-dimensional displays Lighting Image edge detection Robustness Solid modeling

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Multi-camera Trajectory Mining: Database and Evaluation

Multi-camera Trajectory Mining: Database and Evaluation

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International Conference on pattern recognition

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

ISBN: (纸本)9781479952106

In recent years, large-scale video search and mining has been an active research area. Exploring the trajectory of pedestrian of interest in non-overlapping multi-camera network, namely the trajectory mining, is very useful for visual surveillance and criminal investigation. The trajectory mentioned in our work describes the transition of pedestrian among cameras from a macroscopic perspective which is different from the concept in conventional tracking field. In this paper, we collect a database called TMin to promote research and development of trajectory mining. This release of Version 1 contains 1680 images from 30 subjects, all the images are extracted from 6 surveillance videos over two hours, and each subject appears in at least two different cameras. We describe the apparatuses, environments and procedure of the data collection and present baseline performance on the TMin database.

关键词： Cameras Trajectory Databases Image color analysis Data mining Topology Network topology

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A Probabilistic Framework for Multitarget Tracking with Mutual Occlusions

A Probabilistic Framework for Multitarget Tracking with Mutu...

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IEEE Conference on Computer Vision and pattern recognition

作者： Menglong Yang Yiguang Liu Longyin Wen Zhisheng You Stan Z. Li Key Laboratory of Fundamental Synthetic Vision Graphics and Image for National Defense School of Aeronautics and Astronautics & Computer Science Sichuan University Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479951192

Mutual occlusions among targets can cause track loss or target position deviation, because the observation likelihood of an occluded target may vanish even when we have the estimated location of the target. This paper presents a novel probability framework for multitarget tracking with mutual occlusions. The primary contribution of this work is the introduction of a vectorial occlusion variable as part of the solution. The occlusion variable describes occlusion states of the targets. This forms the basis of the proposed probability framework, with the following further contributions: 1) Likelihood: A new observation likelihood model is presented, in which the likelihood of an occluded target is computed by referring to both of the occluded and occluding targets. 2) Priori: Markov random field (MRF) is used to model the occlusion priori such that less likely "circular" or "cascading" types of occlusions have lower priori probabilities. Both the occlusion priori and the motion priori take into consideration the state of occlusion. 3) Optimization: A realtime RJMCMC-based algorithm with a new move type called "occlusion state update" ispresented. Experimental results show that the proposed framework can handle occlusions well, even including long-duration full occlusions, which may cause tracking failures in the traditional methods.

关键词： Target tracking Probabilistic logic Approximation algorithms Cameras Proposals Computational modeling

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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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Exploring Structural Information and Fusing Multiple Features for Person Re-identification

Exploring Structural Information and Fusing Multiple Feature...

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IEEE Conference on Computer Vision and pattern recognition Workshops

作者： Yang Hu Shengcai Liao 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

ISBN: (纸本)9781479909940

Recently, methods with learning procedure have been widely used to solve person re-identification (re-id) problem. However, most existing databases for re-id are small-scale, therefore, over-fitting is likely to occur. To further improve the performance, we propose a novel method by fusing multiple local features and exploring their structural information on different levels. The proposed method is called Structural Constraints Enhanced Feature Accumulation (SCEFA). Three local features (i.e., Hierarchical Weighted Histograms (HWH), Gabor Ternary pattern HSV (GTP-HSV), Maximally Stable Color Regions (MSCR)) are used. Structural information of these features are deeply explored in three levels: pixel, blob, and part. The matching algorithms corresponding to the features are also discussed. Extensive experiments conducted on three datasets: VIPeR, ETHZ and our own challenging dataset MCSSH, show that our approach outperforms stat-of-the-art methods significantly.

关键词： Persons Dataset

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Face Liveness Detection with Component Dependent Descriptor

Face Liveness Detection with Component Dependent Descriptor

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IAPR International Conference on biometrics

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

ISBN: (纸本)9781479903115

Spoofing attacks mainly include printing artifacts, electronic screens and ultra-realistic face masks or models. In this paper, we propose a component-based face coding approach for liveness detection. The proposed method consists of four steps: (1) locating the components of face;(2) coding the low-level features respectively for all the components;(3) deriving the high-level face representation by pooling the codes with weights derived from Fisher criterion;(4) concatenating the histograms from all components into a classifier for identification. The proposed framework makes good use of micro differences between genuine faces and fake faces. Meanwhile, the inherent appearance differences among different components are retained. Extensive experiments on three published standard databases demonstrate that the method can achieve the best liveness detection performance in three databases.

关键词： Face Face histograms LOW LEVEL Classifiers RCE1 gene Masks Database computer information security Spoofing Descriptors

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Real-time High Performance Deformable Model for Face Detection in the Wild

Real-time High Performance Deformable Model for Face Detecti...

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IAPR International Conference on biometrics

作者： Junjie Yan Xucong Zhang Zhen Lei Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479903115

We present an effective deformable part model for face detection in the wild. Compared with previous systems on face detection, there are mainly three contributions. The first is an efficient method for calculating histogram of oriented gradients by pre-calculated lookup tables, which only has read and write memory operations and the feature pyramid can be calculated in real-time. The second is a Sparse Constrained Latent Bilinear Model to simultaneously learn the discriminative deformable part model, and reduce the feature dimension by sparse transformations for efficient inference. The third contribution is a deformable part based cascade, where every stage is a deformable part in the discriminatively learned model. By integrating the three techniques, we demonstrate noticeable improvements over previous state-of-the-art on FDDB with real-time speed, under widely comparisons with both academic and commercial detectors.

关键词： Facial recognition Deformable models Table lookup HIGH-PERFORMANCE cascades

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Face Liveness Detection Using 3D Structure Recovered from a Single Camera

Face Liveness Detection Using 3D Structure Recovered from a ...

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IAPR International Conference on biometrics

作者： Tao Wang Jianwei Yang Zhen Lei Shengcai Liao Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781479903115

Face recognition, which is security-critical, has been widely deployed in our daily life. However, traditional face recognition technologies in practice can be spoofed easily, for example, by using a simple printed photo. In this paper, we propose a novel face liveness detection approach to counter spoofing attacks by recovering sparse 3D facial structure. Given a face video or several images captured from more than two viewpoints, we detect facial landmarks and select key frames. Then, the sparse 3D facial structure can be recovered from the selected key frames. Finally, an Support Vector Machine (SVM) classifier is trained to distinguish the genuine and fake faces. Compared with the previous works, the proposed method has the following advantages. First, it gives perfect liveness detection results, which meets the security requirement of face biometric systems. Second, it is independent on cameras or systems, which works well on different devices. Experiments with genuine faces versus planar photo faces and warped photo faces demonstrate the superiority of the proposed method over the state-of-the-art liveness detection methods.

关键词： Face Face Cameras Facial recognition safety requirements Support Vector Network Frame

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Robust Multi-Resolution Pedestrian Detection in Traffic Scenes

Robust Multi-Resolution Pedestrian Detection in Traffic Scen...

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IEEE Conference on Computer Vision and pattern recognition

作者： Junjie Yan Xucong Zhang Zhen Lei Shengcai Liao Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467364102

The serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection techniques [14, 23]. In this paper, we take pedestrian detection in different resolutions as different but related problems, and propose a Multi-Task model to jointly consider their commonness and differences. The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background. For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. The context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).

关键词： Pedestrians Context modeling miss rate Multi-resolution False-positives vehicle inspection technique Traffic Resolving power

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Towards Pose Robust Face recognition

Towards Pose Robust Face Recognition

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IEEE Conference on Computer Vision and pattern recognition

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

ISBN: (纸本)9781467364102

Most existing pose robust methods are too computational complex to meet practical applications and their performance under unconstrained environments are rarely evaluated. In this paper, we propose a novel method for pose robust face recognition towards practical applications, which is fast, pose robust and can work well under unconstrained environments. Firstly, a 3D deformable model is built and a fast 3D model fitting algorithm is proposed to estimate the pose of face image. Secondly, a group of Gabor filters are transformed according to the pose and shape of face image for feature extraction. Finally, PCA is applied on the pose adaptive Gabor features to remove the redundances and Cosine metric is used to evaluate the similarity. The proposed method has three advantages: (1) The pose correction is applied in the filter space rather than image space, which makes our method less affected by the precision of the 3D model;(2) By combining the holistic pose transformation and local Gabor filtering, the final feature is robust to pose and other negative factors in face recognition;(3) The 3D structure and facial symmetry are successfully used to deal with self-occlusion. Extensive experiments on FERET and PIE show the proposed method outperforms state-of-the-art methods significantly, meanwhile, the method works well on LFW.

关键词： Facial recognition facial symmetry Deformable models Gabor filters Robustness project independence evaluation system three-dimensional displays Feature extraction Image images Imagery (Psychotherapy) Fasting gabor filtering

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