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

共 104 条记录，以下是21-30 订阅

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Subspace learning with frequency regularizer: Its application to face recognition

Subspace learning with frequency regularizer: Its applicatio...

引用

IAPR International Conference on biometrics (ICB)

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

Subspace learning is an important technique to enhance the discriminative ability of feature representation and reduce the dimension to improve its efficiency. Due to limited training samples and the usual high-dimensional feature, subspace learning always suffers from overfitting problem, which affects its generalization performance. One possible method is to introduce prior information as a regularizer to constrain its solution space. Traditional regularizers are usually designed in spatial domain, which usually make the projection smooth. In this work, we propose a frequency regularizer (FR), which suppresses the high frequency energy so that the smooth priori is incorporated. Two representative supervised subspace methods with frequency regularizer, FR-LDA and FR-SR are introduced and further applied to face recognition problem. Extensive experiments on popular face databases validate the effectiveness and superiority of FR based subspace learning compared to traditional subspace learning methods.

关键词： Face recognition Face Databases Accuracy Principal component analysis Learning systems Frequency-domain analysis

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A convolutional neural network combined with aggregate channel feature for face detection 6

A convolutional neural network combined with aggregate chann...

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6th International Conference on Wireless, Mobile and Multi-Media, ICWMMN 2015

作者： Wang, Shuo Yang, Bin Lei, Zhen Wan, Jun Li, Stan Z. School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China China Research and Development Center for Internet of Thing China Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781849199940

Face detection has been studied intensively over the past several decades and achieved great improvements via convolutional neural network (CNN) which has greatly improved the performance in image classification and object detection. In this paper, similar to the idea of R-CNN [1], we present a new method that combines the aggregate channel features (ACF) [2] and CNN for face detection. The proposed method uses ACF to select the possible human face regions and then trains a CNN model to filter out non-face candidates. Then we merge the results of ACF and CNN to get the final detection window(s). Evaluations on two popular face detection benchmark datasets show that our method outperforms the ACF method and has achieved competitive performance against the state-of-the-art algorithms.

关键词： Aggregates

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Adaptively Unified Semi-Supervised Dictionary Learning with Active Points

Adaptively Unified Semi-Supervised Dictionary Learning with ...

引用

International Conference on Computer Vision (ICCV)

作者： Xiaobo Wang Xiaojie Guo Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Chinese Academy of Sciences State Key Laboratory of Information Security Chinese Academy of Sciences

ISBN: (纸本)9781467383929

Semi-supervised dictionary learning aims to construct a dictionary by utilizing both labeled and unlabeled data. To enhance the discriminative capability of the learned dictionary, numerous discriminative terms have been proposed by evaluating either the prediction loss or the class separation criterion on the coding vectors of labeled data, but with rare consideration of the power of the coding vectors corresponding to unlabeled data. In this paper, we present a novel semi-supervised dictionary learning method, which uses the informative coding vectors of both labeled and unlabeled data, and adaptively emphasizes the high confidence coding vectors of unlabeled data to enhance the dictionary discriminative capability simultaneously. By doing so, we integrate the discrimination of dictionary, the induction of classifier to new testing data and the transduction of labels to unlabeled data into a unified framework. To solve the proposed problem, an effective iterative algorithm is designed. Experimental results on a series of benchmark databases show that our method outperforms other state-of-the-art dictionary learning methods in most cases.

关键词： Encoding Dictionaries Solids Learning systems Data models Loss measurement Testing

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A convolutional neural network combined with aggregate channel feature for face detection

A convolutional neural network combined with aggregate chann...

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6th International Conference on Wireless, Mobile and Multi-Media (ICWMMN 2015)

作者： Shuo Wang Bin Yang Zhen Lei Jun Wan Stan Z. Li School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China China Research and Development Center for Internet of Thing China

ISBN: (纸本)9781510822030

关键词： CNN ACF Face detection Facial recognition CLEFT PALATE, CARDIAC DEFECT, GENITAL ANOMALIES, AND ECTRODACTYLY aggregate channel Neural network Image classification Object detection aggregates

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Local Gradient Order pattern for Face Representation and recognition

Local Gradient Order Pattern for Face Representation and Rec...

引用

International Conference on pattern recognition

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

LBP is an effective descriptor for face recognition. LBP encodes the ordinal relationship between the neighborhood samplings and the central one to obtain robust face representation. However, additional information like the difference among neighboring pixels, which may be helpful for face recognition, is ignored. On the other hand, gradient information which enhances the edge response and suppresses the external noise like illumination variation, is usually useful for face recognition. In this paper, we propose a novel face descriptor, namely local gradient order pattern (LGOP), taking into account the ordinal relationship of gradient responses in local region to obtain robust face representation. After pattern encoding, a 2-D histogram is consequently adopted to calculate the occurrence frequency of different patterns and multi-scale histogram features are extracted to represent the face image. We further adopt whitened principal component analysis (WPCA) to reduce the feature dimensionality and improve the computational efficiency. Extensive experiments on FERET, CAS-PEAL and LFW validates the effectiveness of LGOP for both constrained and unconstrained face recognition problems.

关键词： Face Face recognition Histograms Feature extraction Databases Robustness Lighting

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When Face recognition Meets with Deep Learning: An Evaluation of Convolutional Neural Networks for Face recognition

When Face Recognition Meets with Deep Learning: An Evaluatio...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Guosheng Hu Yongxin Yang Dong Yi Josef Kittler William Christmas Stan Z. Li Timothy Hospedales Centre for Vision Speech and Signal Processing University of Surrey UK Indicates equal contribution LEAR team Inria Grenoble Rhone-Alpes Montbonnot France Electronic Engineering and Computer Science Queen Mary University of London UK Chinese Academy of Sciences Center for Biometrics and Security Research & National Laboratory of Pattern Recognition China

Deep learning, in particular Convolutional Neural Network (CNN), has achieved promising results in face recognition recently. However, it remains an open question: why CNNs work well and how to design a 'good' architecture. The existing works tend to focus on reporting CNN architectures that work well for face recognition rather than investigate the reason. In this work, we conduct an extensive evaluation of CNN-based face recognition systems (CNN-FRS) on a common ground to make our work easily reproducible. Specifically, we use public database LFW (Labeled Faces in the Wild) to train CNNs, unlike most existing CNNs trained on private databases. We propose three CNN architectures which are the first reported architectures trained using LFW data. This paper quantitatively compares the architectures of CNNs and evaluates the effect of different implementation choices. We identify several useful properties of CNN-FRS. For instance, the dimensionality of the learned features can be significantly reduced without adverse effect on face recognition accuracy. In addition, a traditional metric learning method exploiting CNN-learned features is evaluated. Experiments show two crucial factors to good CNN-FRS performance are the fusion of multiple CNNs and metric learning. To make our work reproducible, source code and models will be made publicly available.

关键词： Face recognition Face Databases Measurement Training Object recognition Convolutional codes

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Aggregate channel features for multi-view face detection

Aggregate channel features for multi-view face detection

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IEEE International Joint Conference on biometrics (IJCB)

作者： Bin Yang Junjie Yan Zhen Lei Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Chinese Academy of Sciences China

Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones. While many subsequences have improved the work with more powerful learning algorithms, the feature representation used for face detection still can't meet the demand for effectively and efficiently handling faces with large appearance variance in the wild. To solve this bottleneck, we borrow the concept of channel features to the face detection domain, which extends the image channel to diverse types like gradient magnitude and oriented gradient histograms and therefore encodes rich information in a simple form. We adopt a novel variant called aggregate channel features, make a full exploration of feature design, and discover a multi-scale version of features with better performance. To deal with poses of faces in the wild, we propose a multi-view detection approach featuring score re-ranking and detection adjustment. Following the learning pipelines in Viola-Jones framework, the multi-view face detector using aggregate channel features shows competitive performance against state-of-the-art algorithms on AFW and FDDB test-sets, while runs at 42 FPS on VGA images.

关键词： Feature extraction Face Detectors Aggregates Face detection Databases Training

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The Fastest Deformable Part Model for Object Detection

The Fastest Deformable Part Model for Object Detection

引用

IEEE Conference on Computer Vision and pattern recognition

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

ISBN: (纸本)9781479951192

This paper solves the speed bottleneck of deformable part model (DPM), while maintaining the accuracy in detection on challenging datasets. Three prohibitive steps in cascade version of DPM are accelerated, including 2D correlation between root filter and feature map, cascade part pruning and HOG feature extraction. For 2D correlation, the root filter is constrained to be low rank, so that 2D correlation can be calculated by more efficient linear combination of 1D correlations. A proximal gradient algorithm is adopted to progressively learn the low rank filter in a discriminative manner. For cascade part pruning, neighborhood aware cascade is proposed to capture the dependence in neighborhood regions for aggressive pruning. Instead of explicit computation of part scores, hypotheses can be pruned by scores of neighborhoods under the first order approximation. For HOG feature extraction, look-up tables are constructed to replace expensive calculations of orientation partition and magnitude with simpler matrix index operations. Extensive experiments show that (a) the proposed method is 4 times faster than the current fastest DPM method with similar accuracy on Pascal VOC, (b) the proposed method achieves state-of-the-art accuracy on pedestrian and face detection task with frame-rate speed.

关键词： Correlation Acceleration Table lookup Feature extraction Training Deformable models Accuracy

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Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph

Multiple Target Tracking Based on Undirected Hierarchical Re...

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IEEE Conference on Computer Vision and pattern recognition

作者： Longyin Wen Wenbo Li 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

ISBN: (纸本)9781479951192

Multi-target tracking is an interesting but challenging task in computer vision field. Most previous data association based methods merely consider the relationships (e.g. appearance and motion pattern similarities) between detections in local limited temporal domain, leading to their difficulties in handling long-term occlusion and distinguishing the spatially close targets with similar appearance in crowded scenes. In this paper, a novel data association approach based on undirected hierarchical relation hypergraph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed undirected affinity graph. The relationships between different detections across the spatio-temporal domain are considered in a high-order way, which makes the tracker robust to the spatially close targets with similar appearance. Meanwhile, the hierarchical design of the optimization process fuels our tracker to long-term occlusion with more robustness. Extensive experiments on various challenging datasets (i.e. PETS2009 dataset, ParkingLot), including both low and high density sequences, demonstrate that the proposed method performs favorably against the state-of-the-art methods.

关键词： Target tracking Optimization Trajectory Motion segmentation Color Histograms

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A benchmark study of large-scale unconstrained face recognition

A benchmark study of large-scale unconstrained face recognit...

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IEEE International Joint Conference on biometrics (IJCB)

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

Many efforts have been made in recent years to tackle the unconstrained face recognition challenge. For the benchmark of this challenge, the Labeled Faces in theWild (LFW) database has been widely used. However, the standard LFW protocol is very limited, with only 3,000 genuine and 3,000 impostor matches for classification. Today a 97% accuracy can be achieved with this benchmark, remaining a very limited room for algorithm development. However, we argue that this accuracy may be too optimistic because the underlying false accept rate may still be high (e.g. 3%). Furthermore, performance evaluation at low FARs is not statistically sound by the standard protocol due to the limited number of impostor matches. Thereby we develop a new benchmark protocol to fully exploit all the 13,233 LFW face images for large-scale unconstrained face recognition evaluation under both verification and open-set identification scenarios, with a focus at low FARs. Based on the new benchmark, we evaluate 21 face recognition approaches by combining 3 kinds of features and 7 learning algorithms. The benchmark results show that the best algorithm achieves 41.66% verification rates at FAR=0.1%, and 18.07% open-set identification rates at rank 1 and FAR=1%. Accordingly we conclude that the large-scale unconstrained face recognition problem is still largely unresolved, thus further attention and effort is needed in developing effective feature representations and learning algorithms. We thereby release a benchmark tool to advance research in this field.

关键词： Face Benchmark testing Face recognition Protocols Standards Probes Training

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