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Singular Value Decomposition Projection for solving the small sample size problem in face recognition

JOURNAL OF VISUAL COMMUNICATION AND IMAGE representation 2015年 26卷 265-274页

作者： Wang, Changpeng Zhang, Jiangshe Chang, Guodong Ke, Qiao Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

Numerous dimensionality reduction methods have achieved impressive performance in face recognition field due to their potential to exploit the intrinsic structure of images and to enhance the computational efficiency. However, the FR methods based on the existing dimensionality reduction often suffer from small sample size (SSS) problems, where the sample dimensionality is larger than the number of training samples per subject. In recent years, sparse representation based classification (SRC) has been demonstrated to be a powerful framework for robust FR. In this paper, a novel unsupervised dimensionality reduction algorithm, called Singular Value Decomposition Projection (SVDP), is proposed to better fit SRC for handling the SSS problems in FR. In SVDP, a weighted linear transformation matrix is derived from the original data matrix via Singular Value Decomposition. The projection obtained in this way is row-orthonormal and it has some good properties. It makes the solution be robust to small perturbations contained in the data and has better ability to represent various signals. Thus, SVDP could better preserve the discriminant information of the data. based on SVDP, a novel face recognition method SVDP-SRC is designed to enable SRC to achieve better performance via low-dimensional representation of faces. The experiments carried out with some simulated data show that SVDP achieves higher recovery accuracy than several other dimensionality reduction methods. Moreover, the results obtained on three standard face databases demonstrate that SVDP-SRC is quite effective to handle the SSS problems in terms of recognition accuracy. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Face recognition Dimensionality reduction Singular Value Decomposition Small sample size problem sparse representation based classification Transformation matrix Row-orthonormal Recognition accuracy

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sparse discriminative feature selection

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PATTERN RECOGNITION 2015年第5期48卷 1827-1835页

作者： Yan, Hui Yang, Jian Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China

As sparse representation-based classifier (SRC) is developed, it has drawn more and more attentions in dimension reduction. In this paper, we introduce SRC based measurement criterion into feature selection, and then propose a novel method called sparse discriminative feature selection. Our objective function aims to find a subset of features, which minimize the within-class reconstruction residual and simultaneously maximize the between-class reconstruction residual in the subspace of selected features. A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed combinatorial optimization formulation. In particular, our joint selection algorithm adds l(2,1)-norm minimization into the objective function, which reduces the redundant and learns features weights simultaneously. A new iterative algorithm is also developed to optimize the proposed objective function. Experiments on benchmark data sets demonstrate the performance of our feature selection method. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Joint feature selection sparse representation based classification Discriminative learning

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A Robust sparse representation based Face Recognition System for Smartphones

A Robust Sparse Representation based Face Recognition System...

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2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

作者： Abavisani, Mahdi Joneidi, Mohsen Rezaeifar, Shideh Shokouhi, Shahriar Baradaran Iran Univ Sci & Technol Dept Elect Engn Tehran Iran Sharif Univ Technol Dept Elect Engn Tehran Iran Ferdowsi Univ Mashhad Dept Elect Engn Mashhad Iran Iran Univ Sci & Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781509013500

Many research works have been done in face recognition during the last years that indicates the importance of face recognition systems in many applications including identity authentication. In this paper we propose an approach for face recognition which is suitable for unconstrained image acquisition and has a low computational cost. Since in practical applications such as in smartphones, imaging conditions are not limited to existing images in the database, robustness of the recognition algorithm is very important. Here a sparse representation framework is proposed which achieves some degree of robustness. Using double sparse representation the high computational cost of sparsity-based classifications is resolved. The experimental result indicates that the proposed method outperforms other well-known algorithms in terms of robustness and it is still fast enough for real-time applications in smartphones.

关键词： Discriminative Dictionary Learning Face Recognition Fast sparse representation Smartphones sparse representation based classification

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sparse representation with Regularization Term for Face Recognition 1

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1st Chinese Conference on Computer Vision (CCCV)

作者： Ji, Jian Ji, Huafeng Bai, Mengqi Xidian Univ Dept Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (数字)9783662485705

ISBN: (纸本)9783662485705;9783662485699

In recent years there has been a growing interest in the study of sparse representation based classification (SRC) which has obtained great success in face recognition. However, SRC is overly dependent on the size of training samples while overlooking the correlation information that is critical to the real-world face recognition problems. Besides, some method considers the correlation information but overlooks the discriminating ability of sparsity. In this paper, we propose a new method called trace norm sparse representation based classification (TSRC) which introduces a regularization term in the SRC model and considers both sparsity and correlation. The TSRC method can benefits from both l(1)-norm and l(2)-norm, which is flexible and can obtain satisfactory results. Experimental results on 2 face databases clearly show that the proposed TSRC method outperforms many state-of-the-art face recognition methods.

关键词： Face recognition Trace norm sparse representation based classification Sparsity and correlation

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The sparse representation and smoothed L0 algorithm for face recognition

The sparse representation and smoothed L0 algorithm for face...

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Jun-Yevg Zeng Yi-Kui Zhai Jun-Yevg Gan School of Information Engineering Wuyi University Jiangmen China

ISBN: (纸本)9781467372251

The sparse representation based classification (SRC) can effectively improve the face recognition rate. Smoothed l 0 algorithm has much faster calculation speed and requires fewer measured values than the other sparse representation method. In this paper, the sparse representation and smoothed l 0 algorithm for face recognition are presented to improve the face recognition under various conditions such as face disguise, illumination and pose changes, etc. The experiments on the AR, Extended Yale B and FERET face database verify the effectiveness of the presented method. The experimental results show that the face recognition algorithm increases to a certain extent in terms of recognition robustness and time than the original SRC.

关键词： face recognition image classification image representation smoothing methods AR face database Extended Yale B face database FERET face database SRC face disguise face recognition algorithm illumination pose changes recognition robustness smoothed l0 algorithm sparse representation based classification Face recognition sparse representation l0 norm Facial Recognition sparse representation face recognition algorithm SAFETY REVIEW COMMITTEE smoothing methods image representation sparse representation-based classification Image classification illumination Lighting

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A sparse representation based approach for recognition of power system transients

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2014年 30卷 137-144页

作者： Chakraborty, Soumi Chatterjee, Amitava Goswami, Swapan Kumar Meghnad Saha Inst Technol Dept Elect Engn Kolkata 700107 India Jadavpur Univ Dept Elect Engn Kolkata 700032 India

The present work shows how a new genre of problem solution techniques called compressive sensing (Cl) can be utilized for solving power quality problems. Specifically speaking, this work undertakes the power system transient recognition problem and solves it using sparse representation based classification (SRC) technique, a popular, recently emerging technique in Cl. The method employs SRC based classification in combination with a feature extraction procedure, carried out using dual tree complex wavelet transform (DTCWT). The proposed method uses statistical characteristics to extract features from the DTCWT coefficients obtained from each signal and then these extracted features are used as input arrays for the SRC based classifier. The PQ disturbance events considered in this work include four different transient conditions, namely, transients due to capacitor switching, transformer inrush currents, transients due to motor switching and transients due to short circuit faults. The proposed algorithm could achieve perfect classification accuracy of the order of 100% and could comfortably outperform several similar contemporary methods known for identical power system transient classification problems. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Power system transients Compressive sensing sparse representation based classification (SRC) Dual tree complex wavelet transform (DTCWT)

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The comparative analysis l₂-minimisation based description methods

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OPTIK 2014年第1期125卷 374-379页

作者： Qiu, Minna Li, Zhengming Wang, Yaowu Zhu, Qi Biocomp Res Ctr Shenzhen Grad Sch Harbin Inst Technol Shenzhen 518055 Peoples R China Guangdong Polytech Normal Univ Ind Training Ctr Guangzhou Guangdong Peoples R China Shenzhen Grad Sch Harbin Inst Technol Key Lab Urban Planning & Decis Making Simulat Shenzhen 518055 Peoples R China

In this paper, we compare some human face recognition methods which are based l(2)-minimisation description methods. These face recognition methods include the two-phase test sample sparse representation (TPTSSR) method, an improvement to the nearest neighbor classifier and face recognition experiments (INNC), a simple and fast representation-based face recognition method (SFRFR), feature space-based human face image representation and recognition (FSB), collaborative representation classification with regularized least square (CRC_RLS) and relaxed collaborative representation for pattern classification (RCR). We compare all of these methods in the same condition, such as the same database, same test set and same training set. The experimental results show the performance of these six methods. (C) 2013 Published by Elsevier GmbH.

关键词： Face recognition sparse representation based classification TPTSSR

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Expression robust three-dimensional face recognition based on gaussian filter and dual-tree complex wavelet transform

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第1期26卷 193-201页

作者： Wang, Xueqiao Raun, Qiuqi Jin, Yi An, Gaoyun Beijing Jiaotong Univ Beijing Key Lab Adv Informat Sci & Network Techno Inst Informat Sci Beijing 100044 Peoples R China

In this paper, a fully automatic framework is proposed for 3D face recognition and its superiority performance is justified by the FRGC v2 data. For 3D data preprocessing, a new face smoothing method is proposed. Meanwhile, 3D facial representation, which is extracted by the Dual-tree Complex Wavelet Transform (DT-CWT), is introduced to reflect the facial geometry properties. Low redundancy makes it more effective and efficient to describe the discriminant feature in 2.5D range data. In this paper, DT-CWT is used into 2.5D range data in conjunction with the Linear Discriminant Analysis (LDA) to form a rejection classifier, which can quickly eliminate a large number of candidate gallery faces. The remaining faces are then verified using sparse representation based classification. Our method achieves the verification rate of 98.66% on All vs. All experiment at an FAR of 0.1%.

关键词： Dual-tree Complex Wavelet Transform 3D face recognition rejection classifier sparse representation based classification

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Face Recognition via Curvelets and Local Ternary Pattern-based Features

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2014年第4期E97D卷 1004-1007页

作者： Zhou, Lijian Liu, Wanquan Lu, Zhe-Ming Nie, Tingyuan Qingdao Technol Univ Sch Commun & Elect Engn Qingdao 266033 Peoples R China Curtin Univ Dept Comp Perth WA 6102 Australia Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China

In this Letter, a new face recognition approach based on curvelets and local ternary patterns (LIP) is proposed. First, we observe that the curvelet transform is a new anisotropic multi-resolution transform and can efficiently represent edge discontinuities in face images, and that the LTP operator is one of the best texture descriptors in terms of characterizing face image details. This motivated us to decompose the image using the curvelet transform, and extract the features in different frequency bands. As revealed by curvelet transform properties, the highest frequency band information represents the noisy information, so we directly drop it from feature selection. The lowest frequency band mainly contains coarse image information, and thus we deal with it more precisely to extract features as the face's details using LTP. The remaining frequency bands mainly represent edge information, and we normalize them for achieving explicit structure information. Then, all the extracted features are put together as the elementary feature set. With these features, we can reduce the features' dimension using PCA, and then use the sparse sensing technique for face recognition. Experiments on the Yale database, the extended Yale B database, and the CMU PIE database show the effectiveness of the proposed methods.

关键词： face recognition curvelet transform local ternary pattern sparse representation based classification

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Low-rank representation based Action Recognition

Low-rank Representation Based Action Recognition

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Xiangrong Yang, Yang Jia, Hanghua Zhou, Huiyu Jiao, Licheng Xidian Univ Lab Intelligent Percept & Image Understanding Minist Educ Xian Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Queens Univ Belfast Belfast Antrim North Ireland

ISBN: (纸本)9781479914845

Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features extracted from videos still remains to be a challenging problem. In this paper, we propose a novel method named low-rank representation based action recognition to recognize human actions. Given a dictionary, low-rank representation aims at finding the lowestrank representation of all data, which can capture the global data structures. According to its characteristics, low-rank representation is robust against noises. Experimental results demonstrate the effectiveness of the proposed approach on several publicly available datasets.

关键词： human action recognition low-rank representation video representation sparse representation based classification

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