Representations of Face Images and Collaborative Representation Classification for Face Recognition

2017 ◽  
Vol 27 (01) ◽  
pp. 1850017 ◽  
Author(s):  
Hansheng Fang ◽  
Jian Zhang
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Previous Method ◽  
Collaborative Representation ◽  
Linear Combinations ◽  
Accuracy Improvement ◽  
Special Situation ◽  
Face Images ◽  
Training Samples ◽  
Mirror Training

Collaborative representation classification (CRC) was firstly proposed by Zhang et al. [L. Zhang, M. Yang, X. Feng, Y. Ma and D. Zhang, Collaborative Representation based Classification for Face Recognition, Computer Science, 2014]. It was an excellent algorithm for solving face recognition problems. The method suggests that the combination of all original training samples can approach the test samples accurately. But in fact, this does not mean it can well solve complex face recognition problems in some special situation, such as face recognition with varying illuminations and facial expressions. In the paper, we proposed an improvement to previous CRC method. By using a dedicated algorithm to combine the linear combinations of the original and their mirror training samples to represent the test samples, we can get more accurate recognition of test samples. The experimental results show that the proposed method does obtain notable accuracy improvement in comparison with the previous 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.

scholarly journals Histogram-Based CRC for 3D-Aided Pose-Invariant Face Recognition

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 759 ◽  
Author(s):  
Liang Shi ◽  
Xiaoning Song ◽  
Tao Zhang ◽  
Yuquan Zhu
Keyword(s):  
Face Recognition ◽  
Metric Learning ◽  
Test Sample ◽  
Data Uncertainty ◽  
Collaborative Representation ◽  
Face Images ◽  
Virtual Samples ◽  
Pruning Strategy ◽  
Morphable Model ◽  
3D Shapes

Traditional Collaborative Representation-based Classification algorithms for face recognition (CRC) usually suffer from data uncertainty, especially if it includes various poses and illuminations. To address this issue, in this paper, we design a new CRC method using histogram statistical measurement (H-CRC) combined with a 3D morphable model (3DMM) for pose-invariant face classification. First, we fit a 3DMM to raw images in the dictionary to reconstruct the 3D shapes and textures. The fitting results are used to render numerous virtual samples of 2D images that are frontalized from arbitrary poses. In contrast to other distance-based evaluation algorithms for collaborative (or sparse) representation-based methods, the histogram information of all the generated 2D face images is subsequently exploited. Second, we use a histogram-based metric learning to evaluate the most similar neighbours of the test sample, which aims to obtain ideal result for pose-invariant face recognition using the designed histogram-based 3DMM model and online pruning strategy, forming a unified 3D-aided CRC framework. The proposed method achieves desirable classification results that are conducted on a set of well-known face databases, including ORL, Georgia Tech, FERET, FRGC, PIE and LFW.

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 Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition

2015 ◽  
Vol 2015 ◽  
pp. 1-7
Author(s):  
Rong Wang
Keyword(s):  
Face Recognition ◽  
High Accuracy ◽  
Virtual Training ◽  
Face Images ◽  
Squared Error ◽  
Training Samples ◽  
Real World Applications ◽  
The Face ◽  
Error Classification ◽  
Accuracy Performance

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

Using the original and symmetrical face training samples to perform collaborative representation for face recognition

Optik ◽  
2016 ◽  
Vol 127 (4) ◽  
pp. 1900-1904 ◽  
Author(s):  
Zhonghua Liu ◽  
Jiexin Pu ◽  
Qingtao Wu ◽  
Xuhui Zhao
Keyword(s):  
Face Recognition ◽  
Collaborative Representation ◽  
Training Samples

Pose-Invariant Face Synthesis and Recognition via Sparse Coding and Symmetrical Information

2013 ◽  
Vol 437 ◽  
pp. 894-900
Author(s):  
Shuai Zhang ◽  
Hai Rui Wang ◽  
Xiao Li He
Keyword(s):  
Face Recognition ◽  
Sparse Coding ◽  
Estimation Algorithm ◽  
Collaborative Representation ◽  
Synthesis Algorithm ◽  
Face Images ◽  
Symmetric Information ◽  
Recognition Systems ◽  
Pose Variation ◽  
Face Synthesis

Pose variation which brings illumination change, occlusion and non-linear scale variation, dramatically drops the performance of face recognition systems. In this paper, we propose a novel pose invariant face recognition method, in which we build a joint sparse coding scheme to predict face images from a certain pose to another. By introducing autoregressive regularization and symmetric information, our algorithm could achieve high robustness to local misalignment and large pose differences. Besides, we propose a new coarse pose estimation algorithm by collaborative representation classifier, which is very fast and enough accurate for our synthesis algorithm. The experiment results on multi-pose subsets of CMU-PIE and FERET database show the efficiency of the proposed method on multi-pose face recognition.

A Regularized Locality Projection-Based Sparsity Discriminant Analysis for Face Recognition

2018 ◽  
Vol 32 (05) ◽  
pp. 1856006 ◽  
Author(s):  
Chuanbo Yu ◽  
Rencan Nie ◽  
Dongming Zhou
Keyword(s):  
Discriminant Analysis ◽  
Sparse Representation ◽  
Manifold Learning ◽  
Recognition Performance ◽  
Collaborative Representation ◽  
Scatter Matrix ◽  
Linear Discriminant ◽  
Face Images ◽  
Training Samples ◽  
Sparse Presentation

Manifold learning and classifiers based on sparse representation are widely used in pattern recognition. Most of the conventional manifold learning methods are subjected to the choice of parameters. In this paper, we present a Regularized Locality Projection based on Sparsity Discriminant Analysis (RLPSD) method for Feature Extraction (FE) to understand the high-dimensional data such as face images. In RLPSD, firstly, we show the sparse representation of training samples by collaborative representation-based classification (CRC). Secondly, the idea of part optimization based on sparse representation is used to ensure the within-class compactness which combines with the labels of measurements and the weights of sparse presentation can be as small as possible. Finally, whole optimization can be directly obtained without the iteration of local optimization. Meanwhile, the separability information of between-class can be well discriminated by scatter matrix which is similar to Fisher linear discriminant analysis (LDA). The great recognition performance of the proposed method is verified by comparing with the popular algorithms on Yale, ORL, AR and Extended YaleB face databases and Oxford 102 flowers dataset.

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.

scholarly journals The fusion of original and symmetric virtual images for image preprocessing in face recognition and collaborative representation based classification

2017 ◽  
Vol 6 (2) ◽  
pp. 69
Author(s):  
Zhaojie Liu ◽  
Yirui Liu
Keyword(s):  
Face Recognition ◽  
Facial Expressions ◽  
Test Sample ◽  
Negative Influence ◽  
Collaborative Representation ◽  
Image Preprocessing ◽  
Weighted Fusion ◽  
Novel Method ◽  
Fusion Approach ◽  
Virtual Face

Various poses, facial expressions and illuminations are the biggest challenges in the fields of face recognition. To overcome these challenges, we propose a simple yet novel method in this paper by using the approximately symmetrical virtual face. Firstly, based on the symmetrical characteristics of faces, we present the method to generate the virtual faces for all samples in detail. Secondly, the collaborative representation based classification method is performed on both of the original set and virtual set individually. In this way, two kinds of representation residuals of every class can be obtained. Thirdly, an adaptive weighted fusion approach is presented and utilized to integrate those two kinds of representation residuals for face recognition. Lastly, we can obtain the label of the test sample by assigning it to the class with the minimum fused residual. Several experiments are conducted which show that the proposed method not only can greatly improve the classification accuracy, but also can effectively reduce the negative influence of various poses, illuminations, and facial expressions.

scholarly journals Score level fusion in representation-based classification method for face recognition

2020 ◽  
Vol 14 ◽  
pp. 174830262093094
Author(s):  
Zi-Qi Li ◽  
Jun Sun ◽  
Xiao-Jun Wu ◽  
He-Feng Yin
Keyword(s):  
Face Recognition ◽  
Least Square ◽  
Classification Method ◽  
Collaborative Representation ◽  
Score Level Fusion ◽  
Training Samples ◽  
Global Representation ◽  
Face Datasets ◽  
Representation Method ◽  
Level Fusion

Recent years have witnessed the success of representation-based classification method (RBCM) in the domain of face recognition. Collaborative representation-based classification (CRC) and linear regression-based classification (LRC) are two representative approaches. CRC is a global representation method which uses all training samples to represent test samples and utilizes representation residuals to perform classification, whereas LRC is a local representation method which exploits training samples from each class to represent test samples. Related researches indicate that the combination of LRC and CRC can fully exploit the representation residuals produced by them, thus improving the performance of RBCM. However, the representation coefficients obtained by CRC usually contain negative values which may result in overfitting problem. Therefore, to solve this problem to some extent, the combination of LRC and non-negative least square-based classification (NNLSC) is proposed in this paper. Experimental results on benchmark face datasets show that the proposed method is superior to the combination of LRC and CRC and other state-of-the-art RBCMs. The source code of our proposed method is available at https://github.com/li-zi-qi/score-level-fusion-of-NNLS-and-LRC .

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