scholarly journals Integrating Globality and Locality for Robust Representation Based Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zheng Zhang ◽  
Zhengming Li ◽  
Binglei Xie ◽  
Long Wang ◽  
Yan Chen
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Classification Accuracy ◽  
Collaborative Representation ◽  
Testing Sample ◽  
Robust Representation ◽  
High Classification Accuracy ◽  
Training Samples ◽  
Representation Method ◽  
Linear Regression Classification

The representation based classification method (RBCM) has shown huge potential for face recognition since it first emerged. Linear regression classification (LRC) method and collaborative representation classification (CRC) method are two well-known RBCMs. LRC and CRC exploit training samples of each class and all the training samples to represent the testing sample, respectively, and subsequently conduct classification on the basis of the representation residual. LRC method can be viewed as a “locality representation” method because it just uses the training samples of each class to represent the testing sample and it cannot embody the effectiveness of the “globality representation.” On the contrary, it seems that CRC method cannot own the benefit of locality of the general RBCM. Thus we propose to integrate CRC and LRC to perform more robust representation based classification. The experimental results on benchmark face databases substantially demonstrate that the proposed method achieves high classification accuracy.

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 .

Integrating Sparse and Collaborative Representation Classifications for Image Classification

2017 ◽  
Vol 17 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Chunwei Tian ◽  
Guanglu Sun ◽  
Qi Zhang ◽  
Weibing Wang ◽  
Teng Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Linear Combination ◽  
Image Recognition ◽  
Test Sample ◽  
Collaborative Representation ◽  
Training Samples ◽  
Sparse Representation Classification ◽  
The Difference ◽  
Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

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.

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

Double linear regression classification for face recognition

2014 ◽  
Vol 62 (4) ◽  
pp. 288-295 ◽  
Author(s):  
Qingxiang Feng ◽  
Qi Zhu ◽  
Lin-Lin Tang ◽  
Jeng-Shyang Pan
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Linear Regression Classification

MapReduce-Based Parallel Linear Regression for Face Recognition

2014 ◽  
Vol 556-562 ◽  
pp. 2628-2632 ◽  
Author(s):  
Li Sheng Zhang ◽  
Hua Yong Liu ◽  
Da Jiang Lei
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Time Complexity ◽  
Distance Measure ◽  
Classification Algorithm ◽  
Vector Subspace ◽  
Test Image ◽  
Original Response ◽  
Response Vector ◽  
Linear Regression Classification

In order to solve the problem of high time complexity of Linear Regression Classification algorithm,we propose a Mapreduce-based parallel linear regression classification algorithm. The map task uses the test image vector and the vector subspace to predict the response vector for one class, then calculates the distance measure between the predicted response vectory and the original response vector. The reduce task processes the data which are generated by the mappers, the test image is assigned to the nearest class. The experiments shows that the MapReduce-based parallel linear regression classifier can significantly improve the efficiency of Face Recognition.

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