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IEEE Conference on Computer Vision and pattern recognition

作者： Xiangyu Zhu Zhen Lei Xiaoming Liu Hailin Shi Stan Z. Li Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Computer Science and Engineering Michigan State University

ISBN: (纸本)9781467388528

Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45°), lacking the ability to align faces in large poses up to 90°. The challenges are three-fold: Firstly, the commonly used landmark-based face model assumes that all the landmarks are visible and is therefore not suitable for profile views. Secondly, the face appearance varies more dramatically across large poses, ranging from frontal view to profile view. Thirdly, labelling landmarks in large poses is extremely challenging since the invisible landmarks have to be guessed. In this paper, we propose a solution to the three problems in an new alignment framework, called 3D Dense Face Alignment (3DDFA), in which a dense 3D face model is fitted to the image via convolutional neutral network (CNN). We also propose a method to synthesize large-scale training samples in profile views to solve the third problem of data labelling. Experiments on the challenging AFLW database show that our approach achieves significant improvements over state-of-the-art methods.

关键词： face modeling Face Posing Face landmarks Imagery (Psychotherapy) images Alignment

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Histograms of Gabor Ordinal Measures for face representation and recognition

Histograms of Gabor Ordinal Measures for face representation...

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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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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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DEEP BACKGROUND SUBTRACTION WITH GUIDED LEARNING

DEEP BACKGROUND SUBTRACTION WITH GUIDED LEARNING

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IEEE International Conference on Multimedia and Expo

作者： Xuezhi Liang Shengcai Liao Xiaobo Wang Wei Liu Yuxuan Chen Stan Z. Li 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

Recently, convolutional neural networks (CNNs) have been applied in background subtraction (change detection) and gained notable improvements. Two typical methods have been proposed. The first one learns a specific CNN model for each video, but requires manual labeling of training frames on the fly. The other one learns a universal model offline, however, limits its performance in handling various surveillance scenarios. To address these problems, in this paper, a new deep background subtraction method is proposed by introducing a guided learning strategy. The main idea is to learn a specific CNN model for each video to ensure accuracy, but manage to avoid manual labeling. To achieve this, firstly we apply the SubSENSE algorithm [1] to get an initial segmentation, and then an adaptive strategy is designed to select reliable pixels to guide the CNN training. Besides, we also design a simple strategy to automatically select informative frames for guided learning. Experiments on the largest background subtraction benchmark CDnet2014 show that the proposed guided deep learning method outperforms existing state of the arts.

关键词： Background subtraction Change detection Deep learning

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Ordinal palmprint represention for personal identification [represention read representation]

Ordinal palmprint represention for personal identification [...

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Conference on Computer Vision and pattern recognition (CVPR)

作者： Zhenan Sun Tieniu Tan Yunhong Wang S.Z. Li Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China School of Computer Science and Engineering Beihang University China

Palmprint-based personal identification, as a new member in the biometrics family, has become an active research topic in recent years. Although great progress has been made, how to represent palmprint for effective classification is still an open problem. In this paper, we present a novel palmprint representation - ordinal measure, which unifies several major existing palmprint algorithms into a general framework. In this framework, a novel palmprint representation method, namely orthogonal line ordinal features, is proposed. The basic idea of this method is to qualitatively compare two elongated, line-like image regions, which are orthogonal in orientation and generate one bit feature code. A palmprint pattern is represented by thousands of ordinal feature codes. In contrast to the state-of-the-art algorithm reported in the literature, our method achieves higher accuracy, with the equal error rate reduced by 42% for a difficult set, while the complexity of feature extraction is halved.

关键词： biometrics Fingerprint recognition pattern recognition Feature extraction Face recognition Computer vision national security Laboratories Automation Computer science

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Robust eyelid, eyelash and shadow localization for iris recognition

Robust eyelid, eyelash and shadow localization for iris reco...

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IEEE International Conference on Image Processing

作者： Zhaofeng He Tieniu Tan Zhenan Sun Xianchao Qiu Center for Biometrics and Security Research National Laboratory of Patten RecognitionInstitute of Automation Chinese Academy and Sciences Beijing China

Eyelids, eyelashes and shadows are three major challenges for effective iris segmentation, which have not been adequately addressed in the current literature. In this paper, we present a novel method to localize each of them. First, a novel coarse-line to fine-parabola eyelid fitting scheme is developed for accurate and fast eyelid localization. Then, a smart prediction model is established to determine an appropriate threshold for eyelash and shadow detection. Experimental results on the challenging CASIA-IrisV3-Lamp iris image database demonstrate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.

关键词： Robustness Eyelids Eyelashes Iris recognition Image edge detection Predictive models Image segmentation biometrics Shape Filters

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An improved coupled spectral regression for heterogeneous face recognition

An improved coupled spectral regression for heterogeneous fa...

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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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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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2D–3D face matching using CCA

2D–3D face matching using CCA

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International Conference on Automatic Face and Gesture recognition

作者： Weilong Yang Dong Yi Zhen Lei Jitao Sang Stan Z. Li Center for Biometrics Security Research & National Laboratory of Pattern Recognitionage Institute of Automation Chinese Academy and Sciences Beijing China

In recent years, 3D face recognition has obtained much attention. Using 2D face image as probe and 3D face data as gallery is an alternative method to deal with computation complexity, expensive equipment and fussy pretreatment in 3D face recognition systems. In this paper we propose a learning based 2D-3D face matching method using the CCA to learn the mapping between 2D face image and 3D face data. This method makes it possible to match the on-site 2D face image with enrolled 3D face data. Our 2D-3D face matching method decreased the computation complexity drastically compared to the conventional 3D-3D face matching while keeping relative high recognition rate. Furthermore, to simplify the mapping between 2D face image and 3D face data, a patch based strategy is proposed to boost the accuracy of matching. And the kernel method is also evaluated to reveal the non-linear relationship. The experiment results show that CCA based method has good performance and patch based method has significant improvement compared to the holistic method.

关键词： Face recognition Shape Probes Cameras Lighting pattern matching biometrics Data security national security Laboratories

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SADet: Learning an efficient and accurate pedestrian detector

arXiv

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arXiv 2020年

作者： Zhuang, Chubin Lei, Zhen Li, Stan Z. Center for Biometrics and Security Research and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Although the anchor-based detectors have taken a big step forward in pedestrian detection, the overall performance of algorithm still needs further improvement for practical applications, e.g., a good trade-off between the accuracy and efficiency. To this end, this paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector, forming a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection, which includes three main improvements. Firstly, we optimize the sample generation process by assigning soft tags to the outlier samples to generate semi-positive samples with continuous tag value between 0 and 1, which not only produces more valid samples, but also strengthens the robustness of the model. Secondly, a novel center-IoU loss is applied as a new regression loss for bounding box regression, which not only retains the good characteristics of IoU loss, but also solves some defects of it. Thirdly, we also design Cosine-NMS for the postprocess of predicted bounding boxes, and further propose adaptive anchor matching to enable the model to adaptively match the anchor boxes to full or visible bounding boxes according to the degree of occlusion, making the NMS and anchor matching algorithms more suitable for occluded pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of 20 FPS for VGA-resolution images (640×480) on challenging pedestrian detection benchmarks, i.e., CityPersons, Caltech, and human detection benchmark CrowdHuman, leading to a new attractive pedestrian detector. Copyright © 2020, The Authors. All rights reserved.

关键词： Economic and social effects

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