Using the original and 'symmetrical face' training samples to perform representation based two-step face recognition

doi:10.1016/J.PATCOG.2012.11.003

Home
/
Papers
/
Using the original and 'symmetrical face' training samples to perform representation based two-step face recognition

Journal Article•DOI•

Using the original and 'symmetrical face' training samples to perform representation based two-step face recognition

Yong Xu¹, Xingjie Zhu¹, Zhengming Li¹, Guang-Hai Liu², Yuwu Lu¹, Hong Liu³ - Show less +2 more•Institutions (3)

Harbin Institute of Technology¹, Guangxi Normal University², Peking University³

01 Apr 2013-Pattern Recognition (Pergamon)-Vol. 46, Iss: 4, pp 1151-1158

TL;DR: This paper proposes to exploit the symmetry of the face to generate new samples and devise a representation based method to perform face recognition that outperforms state-of-the-art face recognition methods including the sparse representation classification (SRC), linear regression classification (LRC), collaborative representation (CR) and two-phase test sample sparse representation (TPTSSR).

read less

About: This article is published in Pattern Recognition.The article was published on 2013-04-01. It has received 160 citations till now. The article focuses on the topics: Three-dimensional face recognition & Face detection.

...read moreread less

Citations

PDF

Open Access

More filters

Journal Article•DOI•

A New Discriminative Sparse Representation Method for Robust Face Recognition via $l_{2}$ Regularization

[...]

Yong Xu¹, Zuofeng Zhong¹, Jian Yang², Jane You³, David Zhang³ - Show less +1 more•Institutions (3)

Harbin Institute of Technology¹, Nanjing University², Hong Kong Polytechnic University³

01 Oct 2017-IEEE Transactions on Neural Networks

TL;DR: A novel discriminative sparse representation method is proposed and its noticeable performance in image classification is demonstrated by the experimental results, and the proposed method outperforms the existing state-of-the-art sparse representation methods.

...read moreread less

Abstract: Sparse representation has shown an attractive performance in a number of applications. However, the available sparse representation methods still suffer from some problems, and it is necessary to design more efficient methods. Particularly, to design a computationally inexpensive, easily solvable, and robust sparse representation method is a significant task. In this paper, we explore the issue of designing the simple, robust, and powerfully efficient sparse representation methods for image classification. The contributions of this paper are as follows. First, a novel discriminative sparse representation method is proposed and its noticeable performance in image classification is demonstrated by the experimental results. More importantly, the proposed method outperforms the existing state-of-the-art sparse representation methods. Second, the proposed method is not only very computationally efficient but also has an intuitive and easily understandable idea. It exploits a simple algorithm to obtain a closed-form solution and discriminative representation of the test sample. Third, the feasibility, computational efficiency, and remarkable classification accuracy of the proposed $l_{2}$ regularization-based representation are comprehensively shown by extensive experiments and analysis. The code of the proposed method is available at http://www.yongxu.org/lunwen.html .

...read moreread less

171 citations

Cites background or methods from "Using the original and 'symmetrical..."

...However, as shown in [18], [20], and [24], by exploiting a special means, l2 regularization can also obtain satisfactory sparse property....
[...]
...For example, both robust regression for classification [23] and two-phase test sample sparse representation [20], [24] are l2 regularization-based representation methods....
[...]
...Especially, the elaborated algorithms of l2 regularization-based representation, such as the ones designed in [20] and [24], can obtain quite sparse representation coefficients, and flexibly control the extent of sparsity....
[...]

Journal Article•DOI•

Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification

[...]

Yong Xu, Zheng Zhang, Guangming Lu¹, Jian Yang²•Institutions (2)

Harbin Institute of Technology¹, Nanjing University of Science and Technology²

01 Jun 2016-Pattern Recognition

TL;DR: A novel method to automatically produce approximately axis-symmetrical virtual face images that is mathematically very tractable and quite easy to implement and verified in comparison with state-of-the-art dictionary learning algorithms.

...read moreread less

111 citations

Cites background or methods from "Using the original and 'symmetrical..."

...8, one can see that our method in most cases can achieve higher classification accuracies than LCKSVD, DKSVD, SFTS, MFTS, ICR, MFL and RNVS....
[...]
...For example, symmetrical face images proposed in [11] are very beneficial to overcome the problem of varying poses and illuminations....
[...]
...For example, symmetrical face training samples (SFTS) [11], inter-class relationship (ICR) [13], random noise based virtual sample (RNVS) [14] and mirror face training samples (MFTS) [12] were all proposed for face recognition....
[...]
...SFTS Symmetrical face training samples [11] MFTS Mirror face training samples [3] ICR Inter-class relationship [13] MFL Metaface learning [59] RNVS Random noise based virtual sample [14]...
[...]

Journal Article•DOI•

Integrate the original face image and its mirror image for face recognition

[...]

Yong Xu¹, Xuelong Li², Jian Yang³, David Zhang⁴•Institutions (4)

Harbin Institute of Technology¹, Chinese Academy of Sciences², Nanjing University of Science and Technology³, Hong Kong Polytechnic University⁴

01 May 2014-Neurocomputing

TL;DR: This paper proposes a scheme to produce the mirror image of the face and integrate the original face image and its mirror image for representation-based face recognition and shows that the proposed scheme can greatly improve the accuracy of the representation- based classification methods.

...read moreread less

86 citations

Cites background or methods or result from "Using the original and 'symmetrical..."

...8 and 9 show some reasonable and unreasonable “symmetrical” face images obtained in [15]....
[...]
...However, it should be pointed out that in real-world face recognition applications, a large number of face images are not symmetrical images due to non-frontal and non-neutral pose [15]....
[...]
...As shown in [15], these virtual face images can somewhat reflect possible variation in pose and scale of the face....
[...]
...The scheme proposed in [15] is able to obtain a virtual axis-symmetrical face image....
[...]
...proposed an approach to generate “symmetrical” face images and exploited both the original and “symmetrical” face images to recognize the subject [15]....
[...]

Journal Article•DOI•

Multiple representations and sparse representation for image classification

[...]

Yong Xu, Bob Zhang¹, Zuofeng Zhong²•Institutions (2)

University of Macau¹, Harbin Institute of Technology²

15 Dec 2015-Pattern Recognition Letters

TL;DR: The image classification experiments show that the simultaneous use of the proposed novel representations and original images can obtain a much higher accuracy?than the use of only the original images.

...read moreread less

78 citations

Journal Article•DOI•

Adaptive appearance model tracking for still-to-video face recognition

[...]

M. Ali Akber Dewan¹, Eric Granger², Gian Luca Marcialis³, Robert Sabourin², Fabio Roli³ - Show less +1 more•Institutions (3)

Athabasca University¹, École de technologie supérieure², University of Cagliari³

01 Jan 2016-Pattern Recognition

TL;DR: Simulation results obtained with the Chokepoint video dataset indicate that the proposed method provides a significantly higher level of performance compared state-of-the-art systems when a single reference still per individual is available for matching.

...read moreread less

60 citations

Cites background from "Using the original and 'symmetrical..."

...Multiple virtual views are synthesized by linear shape prediction [29], mesh warping [30], morphing [31], symmetry property [32], partitioning a face in several sub-images [33], affine transformation [34], noise perturbation [35], shifting [36], and active appearance model [37]....
[...]

1
2
3
4
…
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

Collapse

References

PDF

Open Access

More filters

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

[...]

이화영

01 Jan 2015

12,972 citations

"Using the original and 'symmetrical..." refers methods in this paper

...4 shows a test sample (4(a)) that is erroneously and correctly classified by the collaborative representation (CR) method proposed in [47] and our method, respectively....
[...]
...Besides our proposed method was tested, several state-of-the-art face recognition methods such as two-phase test sample sparse representation (TPTSSR) [45], collaborative representation (CR) proposed in [47], the feature space-based representation method proposed in [49], sparse representation classification (SRC) proposed in [50] and linear regression classification (LRC) [51]were also tested....
[...]

Journal Article•DOI•

Robust Face Recognition via Sparse Representation

[...]

John Wright¹, Allen Y. Yang², Arvind Ganesh¹, S. Shankar Sastry², Yi Ma¹ - Show less +1 more•Institutions (2)

University of Illinois at Urbana–Champaign¹, University of California, Berkeley²

01 Feb 2009-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.

...read moreread less

Abstract: We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

...read moreread less

9,658 citations

Journal Article•DOI•

Two-dimensional PCA: a new approach to appearance-based face representation and recognition

[...]

Jian Yang, David Zhang, Alejandro F. Frangi¹, Jingyu Yang²•Institutions (2)

University of Zaragoza¹, Nanjing University of Science and Technology²

01 Jan 2004-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation that is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction.

...read moreread less

Abstract: In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.

...read moreread less

3,439 citations

Journal Article•DOI•

Face recognition: a convolutional neural-network approach

[...]

Steve Lawrence¹, C.L. Giles², Ah Chung Tsoi³, Andrew D. Back³•Institutions (3)

Princeton University¹, Penn State College of Information Sciences and Technology², University of Queensland³

01 Jan 1997-IEEE Transactions on Neural Networks

TL;DR: A hybrid neural-network for human face recognition which compares favourably with other methods and analyzes the computational complexity and discusses how new classes could be added to the trained recognizer.

...read moreread less

Abstract: We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

...read moreread less

2,954 citations

"Using the original and 'symmetrical..." refers background in this paper

...However, up to now, face recognition is still faced with a number of challenges such as varying illumination, facial expression and poses [10–16]....
[...]

Proceedings Article•DOI•

Sparse representation or collaborative representation: Which helps face recognition?

[...]

Lei Zhang¹, Meng Yang¹, Xiangchu Feng²•Institutions (2)

Hong Kong Polytechnic University¹, Xidian University²

06 Nov 2011

TL;DR: This paper indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification, and proposes a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS).

...read moreread less

Abstract: As a recently proposed technique, sparse representation based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. While the importance of sparsity is much emphasized in SRC and many related works, the use of collaborative representation (CR) in SRC is ignored by most literature. However, is it really the l 1 -norm sparsity that improves the FR accuracy? This paper devotes to analyze the working mechanism of SRC, and indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS). The extensive experiments clearly show that CRC_RLS has very competitive classification results, while it has significantly less complexity than SRC.

...read moreread less

2,001 citations