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 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.

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.

Using the Original and Symmetrical Face Test Samples to Perform Two-Step Collaborative Representation for Face Recognition

2018 ◽  
Vol 33 (02) ◽  
pp. 1956001 ◽  
Author(s):  
Zhonghua Liu ◽  
Lin Zhang ◽  
Jiexin Pu ◽  
Gang Liu ◽  
Sen Liu
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Classification Scheme ◽  
Test Sample ◽  
Collaborative Representation ◽  
Score Level Fusion ◽  
Face Classification ◽  
The Face ◽  
Number Of Classes ◽  
Level Fusion

Face recognition using sparse representation-based classification (SRC) is a new hot technique in recent years. However, the research indicates that it is the collaborative representation but not the [Formula: see text]-norm sparsity that makes SRC powerful for face classification. Consequently, we propose a simple yet much more efficient face classification scheme, namely two-step collaborative representation-based classification (TSCRC) method. First, we exploit the symmetry of the face to generate new images of each test sample. Then, the original and new generated test samples are, respectively, used to perform TSCRC, which ultimately uses a small number of classes that are near to the test sample to represent and classify it. Finally, the score level fusion is taken to perform classification recognition. The experimental results clearly show that the proposed method has very competitive classification results.

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.

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.

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.

scholarly journals Illumination invariant face recognition using dual-tree complex wavelet transform in logarithm domain

2019 ◽  
Vol 70 (2) ◽  
pp. 113-121
Author(s):  
Guang Yi Chen ◽  
Tien D. Bui ◽  
Adam Krzyzak
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Collaborative Representation ◽  
Face Database ◽  
Complex Wavelet Transform ◽  
Illumination Invariant ◽  
Face Images ◽  
The Face ◽  
Zero Values ◽  
Complex Wavelet

Abstract In this article, we develop a new algorithm for illumination invariant face recognition. We first transform the face images to the logarithm domain, which makes the dark regions brighter. We then use dual-tree complex wavelet transform to generate face images that are approximately invariant to illumination changes and use collaborative representation-based classifier to classify the unknown faces to one known class. We set the approximation sub-band and the highest two DTCWT coefficient sub-bands to zero values before the inverse DTCWT transform is performed. Experimental results demonstrate that our proposed method improves upon a few existing methods under both the noise-free and noisy environments for the Extended Yale Face Database B and the CMU-PIE face database.

scholarly journals Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 878
Author(s):  
C. T. J. Dodson ◽  
John Soldera ◽  
Jacob Scharcanski
Keyword(s):  
Face Recognition ◽  
Cyber Security ◽  
Probability Distributions ◽  
Geodesic Distance ◽  
Recognition Method ◽  
Frequency Distributions ◽  
Face Images ◽  
User Access ◽  
Multidimensional Methods ◽  
Joint Probabilities

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

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