Test Sample Oriented Dictionary Learning for Face Recognition

2016 ◽  
Vol 25 (04) ◽  
pp. 1650017 ◽  
Author(s):  
Zhengming Li
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Sparse Coding ◽  
State Of The Art ◽  
Linear Representation ◽  
Test Sample ◽  
Basic Model ◽  
Linear Classifier ◽  
Learning Procedure ◽  
Training Samples

Dictionary learning (DL) algorithms have shown very good performance in face recognition. However, conventional DL algorithms exploit only the training samples to obtain the dictionary and totally neglect the test sample in the learning procedure. As a result, if DL is associated with the linear representation of test sample, DL may be able to perform better in classifying the test samples than conventional DL algorithms. In this paper, we propose a test sample oriented dictionary learning (TSODL) algorithm for face recognition. We combine the linear representation (including the [Formula: see text]-norm, [Formula: see text]-norm and [Formula: see text]-norm) of a test sample and the basic model of DL to learn a single dictionary for each test sample. Thus, it can simultaneously obtain the dictionary and representation coefficients of the test sample by minimizing only one objective function. In order to make the learning procedure more efficient, we initialize a dictionary for the new test sample by selecting from the dictionaries of previous test samples. The experimental results show that the TSODL algorithm can classify test samples more accurately than some of the state-of-the-art DL and sparse coding algorithms by using a linear classifier method on three public face databases.

Improved LRC Based on Combined Virtual Training Samples for Face Recognition

2016 ◽  
Vol 30 (10) ◽  
pp. 1656006 ◽  
Author(s):  
Wei Huang ◽  
Xiaohui Wang ◽  
Yinghui Zhu ◽  
Gengzhong Zheng
Keyword(s):  
Face Recognition ◽  
Objective Function ◽  
Linear Representation ◽  
Test Sample ◽  
Virtual Training ◽  
Virtual Test ◽  
Training Samples ◽  
Alternating Minimization Algorithm ◽  
Virtual Samples ◽  
The Face

Lack of training samples always affects the performance and robustness of face recognition. Generating virtual samples is one of effective methods to expand the training set. When the virtual samples are able to simulate the variations of facial images including variations of illuminations, facial postures and the facial expressions, the robustness will be enhanced and the accuracy will be improved obviously in the face recognition problem. In this paper, an improved linear representation-based classification combined virtual samples (ILRCVS) is proposed. First, we design a new objective function that simultaneously considers the information of the virtual training samples and the virtual test sample. Second, an alternating minimization algorithm is proposed to solve the optimization problem of the objective function. Finally, a new classification criterion combined with the virtual training and test sample is proposed. Experimental results on the Georgia Tech, FERET and Yale B face databases show that the proposed method is more robust than three state-of-the-art face recognition methods, LRC, SRC and CRC.

scholarly journals A Dictionary Learning Algorithm Based on Dictionary Reconstruction and Its Application in Face Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shijun Zheng ◽  
Yongjun Zhang ◽  
Wenjie Liu ◽  
Yongjie Zou ◽  
Xuexue Zhang
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Coefficient Matrix ◽  
Reconstruction Method ◽  
Original Training ◽  
Locality Constraint ◽  
Training Samples ◽  
Influence Of Noise

In recent years, dictionary learning has received more and more attention in the study of face recognition. However, most dictionary learning algorithms directly use the original training samples to learn the dictionary, ignoring noise existing in the training samples. For example, there are differences between different images of the same subject due to changes in illumination, expression, etc. To address the above problems, this paper proposes the dictionary relearning algorithm (DRLA) based on locality constraint and label embedding, which can effectively reduce the influence of noise on the dictionary learning algorithm. In our proposed dictionary learning algorithm, first, the initial dictionary and coding coefficient matrix are directly obtained from the training samples, and then the original training samples are reconstructed by the product of the initial dictionary and coding coefficient matrix. Finally, the dictionary learning algorithm is reapplied to obtain a new dictionary and coding coefficient matrix, and the newly obtained dictionary and coding coefficient matrix are used for subsequent image classification. The dictionary reconstruction method can partially eliminate noise in the original training samples. Therefore, the proposed algorithm can obtain more robust classification results. The experimental results demonstrate that the proposed algorithm performs better in recognition accuracy than some state-of-the-art algorithms.

Virtual samples based robust block-diagonal dictionary learning for face recognition

2021 ◽  
Vol 25 (5) ◽  
pp. 1273-1290
Author(s):  
Shuangxi Wang ◽  
Hongwei Ge ◽  
Jinlong Yang ◽  
Shuzhi Su
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Recognition Performance ◽  
Small Sample Size ◽  
Small Sample ◽  
Low Rank ◽  
Negative Effects ◽  
Linear Classifier ◽  
Virtual Samples ◽  
Block Diagonal

It is an open question to learn an over-complete dictionary from a limited number of face samples, and the inherent attributes of the samples are underutilized. Besides, the recognition performance may be adversely affected by the noise (and outliers), and the strict binary label based linear classifier is not appropriate for face recognition. To solve above problems, we propose a virtual samples based robust block-diagonal dictionary learning for face recognition. In the proposed model, the original samples and virtual samples are combined to solve the small sample size problem, and both the structure constraint and the low rank constraint are exploited to preserve the intrinsic attributes of the samples. In addition, the fidelity term can effectively reduce negative effects of noise (and outliers), and the ε-dragging is utilized to promote the performance of the linear classifier. Finally, extensive experiments are conducted in comparison with many state-of-the-art methods on benchmark face datasets, and experimental results demonstrate the efficacy of the proposed method.

Robust and discriminative dictionary learning for face recognition

2018 ◽  
Vol 16 (02) ◽  
pp. 1840004 ◽  
Author(s):  
Guojun Lin ◽  
Meng Yang ◽  
Linlin Shen ◽  
Mingzhong Yang ◽  
Mei Xie
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Dictionary Learning ◽  
State Of The Art ◽  
Face Image ◽  
Superior Performance ◽  
Face Images ◽  
Proposed Model ◽  
Classification Capability ◽  
Discriminative Dictionary Learning

For face recognition, conventional dictionary learning (DL) methods have some disadvantages. First, face images of the same person vary with facial expressions and pose, illumination and disguises, so it is hard to obtain a robust dictionary for face recognition. Second, they don’t cover important components (e.g., particularity and disturbance) completely, which limit their performance. In the paper, we propose a novel robust and discriminative DL (RDDL) model. The proposed model uses sample diversities of the same face image to learn a robust dictionary, which includes class-specific dictionary atoms and disturbance dictionary atoms. These atoms can well represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which improves effectively the classification capability of the dictionary. The proposed RDDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.

Nonnegative Sparse Probabilistic Estimation for Single Sample Face Recognition

2020 ◽  
Vol 34 (12) ◽  
pp. 2056008
Author(s):  
Shuhuan Zhao
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Real Life ◽  
Test Sample ◽  
Single Sample ◽  
Collaborative Representation ◽  
Adaptive Variation ◽  
Training Set ◽  
Probabilistic Estimation ◽  
Training Samples

Face recognition (FR) is a hotspot in pattern recognition and image processing for its wide applications in real life. One of the most challenging problems in FR is single sample face recognition (SSFR). In this paper, we proposed a novel algorithm based on nonnegative sparse representation, collaborative presentation, and probabilistic graph estimation to address SSFR. The proposed algorithm is named as Nonnegative Sparse Probabilistic Estimation (NNSPE). To extract the variation information from the generic training set, we first select some neighbor samples from the generic training set for each sample in the gallery set and the generic training set can be partitioned into some reference subsets. To make more meaningful reconstruction, the proposed method adopts nonnegative sparse representation to reconstruct training samples, and according to the reconstruction coefficients, NNSPE computes the probabilistic label estimation for the samples of the generic training set. Then, for a given test sample, collaborative representation (CR) is used to acquire an adaptive variation subset. Finally, the NNSPE classifies the test sample with the adaptive variation subset and probabilistic label estimation. The experiments on the AR and PIE verify the effectiveness of the proposed method both in recognition rates and time cost.

scholarly journals Integrating the Symmetry Image and Improved Sparse Representation for Railway Fastener Classification and Defect Recognition

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jiajia Liu ◽  
Bailin Li ◽  
Ying Xiong ◽  
Biao He ◽  
Li Li
Keyword(s):  
Sparse Representation ◽  
State Of The Art ◽  
Identification Problem ◽  
Test Sample ◽  
Computationally Efficient ◽  
Railway Traffic ◽  
Training Samples ◽  
Inspection Systems ◽  
Representation Method ◽  
Defect Recognition

The detection of fastener defects is an important task for ensuring the safety of railway traffic. The earlier automatic inspection systems based on computer vision can detect effectively the completely missing fasteners, but they have weaker ability to recognize the partially worn ones. In this paper, we propose a method for detecting both partly worn and completely missing fasteners, the proposed algorithm exploits the first and second symmetry sample of original testing fastener image and integrates them for improved representation-based fastener recognition. This scheme is simple and computationally efficient. The underlying rationales of the scheme are as follows: First, the new virtual symmetrical images really reflect some possible appearance of the fastener; then the integration of two judgments of the symmetrical sample for fastener recognition can somewhat overcome the misclassification problem. Second, the improved sparse representation method discarding the training samples that are “far” from the test sample and uses a small number of samples that are “near” to the test sample to represent the test sample, so as to perform classification and it is able to reduce the side-effect of the error identification problem of the original fastener image. The experimental results show that the proposed method outperforms state-of-the-art fastener recognition methods.

scholarly journals Fusing Two Kinds of Virtual Samples for Small Sample Face Recognition

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Minna Qiu ◽  
Jian Zhang ◽  
Jiayan Yang ◽  
Liying Ye
Keyword(s):  
Face Recognition ◽  
Test Sample ◽  
Recognition System ◽  
Training Sample ◽  
Small Sample ◽  
Storage Space ◽  
Training Sample Size ◽  
Face Recognition System ◽  
Training Samples ◽  
Virtual Samples

Face recognition has become a very active field of biometrics. Different pictures of the same face might include various changes of expressions, poses, and illumination. However, a face recognition system usually suffers from the problem that nonsufficient training samples cannot convey these possible changes effectively. The main reason is that a system has only limited storage space and limited time to capture training samples. Many previous literatures ignored the problem of nonsufficient training samples. In this paper, we overcome the insufficiency of training sample size problem by fusing two kinds of virtual samples and the original samples to perform small sample face recognition. The two used kinds of virtual samples are mirror faces and symmetrical faces. Firstly, we transform the original face image to obtain mirror faces and symmetrical faces. Secondly, we fuse these two kinds of virtual samples to achieve the matching scores between the test sample and each class. Finally, we integrate the matching scores to get the final classification results. We compare the proposed method with the single virtual sample augment methods and the original representation-based classification. The experiments on various face databases show that the proposed scheme achieves the best accuracy among the representation-based classification methods.

scholarly journals Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition

2019 ◽  
Vol 9 (6) ◽  
pp. 1189 ◽  
Author(s):  
Biwei Ding ◽  
Hua Ji
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Principal Components ◽  
Dictionary Learning ◽  
Recognition Rate ◽  
Computation Time ◽  
Kernel Discriminant Analysis ◽  
Training Samples ◽  
Sparse Representation Classifier ◽  
Facial Changes

In this paper, a kernel-based robust disturbance dictionary (KRDD) is proposed for face recognition that solves the problem in modern dictionary learning in which significant components of signal representation cannot be entirely covered. KRDD can effectively extract the principal components of the kernel by dimensionality reduction. KRDD not only performs well with occluded face data, but is also good at suppressing intraclass variation. KRDD learns the robust disturbance dictionaries by extracting and generating the diversity of comprehensive training samples generated by facial changes. In particular, a basic dictionary, a real disturbance dictionary, and a simulated disturbance dictionary are acquired to represent data from distinct subjects to fully represent commonality and disturbance. Two of the disturbance dictionaries are modeled by learning few kernel principal components of the disturbance changes, and then the corresponding dictionaries are obtained by kernel discriminant analysis (KDA) projection modeling. Finally, extended sparse representation classifier (SRC) is used for classification. In the experimental results, KRDD performance displays great advantages in recognition rate and computation time compared with many of the most advanced dictionary learning methods for face recognition.

scholarly journals A Collaborative Neighbor Representation Based Face Recognition Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Zhengming Li ◽  
Qi Zhu ◽  
Binglei Xie ◽  
Jian Cao ◽  
Jin Zhang
Keyword(s):  
Face Recognition ◽  
Linear Combination ◽  
Test Sample ◽  
Reconstruction Error ◽  
Recognition Algorithm ◽  
Second Phase ◽  
Two Phase ◽  
Alternative Scheme ◽  
Training Samples ◽  
Discrimination Information

We propose a new collaborative neighbor representation algorithm for face recognition based on a revised regularized reconstruction error (RRRE), called the two-phase collaborative neighbor representation algorithm (TCNR). Specifically, the RRRE is the division of  l2-norm of reconstruction error of each class into a linear combination of  l2-norm of reconstruction coefficients of each class, which can be used to increase the discrimination information for classification. The algorithm is as follows: in the first phase, the test sample is represented as a linear combination of all the training samples by incorporating the neighbor information into the objective function. In the second phase, we use thekclasses to represent the test sample and calculate the collaborative neighbor representation coefficients. TCNR not only can preserve locality and similarity information of sparse coding but also can eliminate the side effect on the classification decision of the class that is far from the test sample. Moreover, the rationale and alternative scheme of TCNR are given. The experimental results show that TCNR algorithm achieves better performance than seven previous algorithms.

scholarly journals Laplace Graph Embedding Class Specific Dictionary Learning for Face Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Li Wang ◽  
Yan-Jiang Wang ◽  
Bao-Di Liu
Keyword(s):  
Face Recognition ◽  
Dictionary Learning ◽  
Graph Embedding ◽  
Small Sample ◽  
Superior Performance ◽  
Collaborative Representation ◽  
Training Samples ◽  
Arbitrary Kernel ◽  
Fitting Error ◽  
Dictionary Training

The sparse representation based classification (SRC) method and collaborative representation based classification (CRC) method have attracted more and more attention in recent years due to their promising results and robustness. However, both SRC and CRC algorithms directly use the training samples as the dictionary, which leads to a large fitting error. In this paper, we propose the Laplace graph embedding class specific dictionary learning (LGECSDL) algorithm, which trains a weight matrix and embeds a Laplace graph to reconstruct the dictionary. Firstly, it can increase the dimension of the dictionary matrix, which can be used to classify the small sample database. Secondly, it gives different dictionary atoms with different weights to improve classification accuracy. Additionally, in each class dictionary training process, the LGECSDL algorithm introduces the Laplace graph embedding method to the objective function in order to keep the local structure of each class, and the proposed method is capable of improving the performance of face recognition according to the class specific dictionary learning and Laplace graph embedding regularizer. Moreover, we also extend the proposed method to an arbitrary kernel space. Extensive experimental results on several face recognition benchmark databases demonstrate the superior performance of our proposed algorithm.

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