An Effective Method for Face Recognition by Creating Virtual Training Samples Based on Pixel Processing

2018 ◽  
Author(s):  
Gang He ◽  
Xiujuan Yuan ◽  
Wei Zhang ◽  
Shi Yan
Keyword(s):  
Face Recognition ◽  
Virtual Training ◽  
Training Samples

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.

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.

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 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.

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
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