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.

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.

scholarly journals An Analysis of Face Recognition under Face Mask Occlusions

2021 ◽  
Author(s):  
Susith Hemathilaka ◽  
Achala Aponso
Keyword(s):  
Face Recognition ◽  
Rapid Development ◽  
Face Mask ◽  
Large Area ◽  
Daily Lives ◽  
Lighting Conditions ◽  
Intractable Problems ◽  
The Face ◽  
Recognition Systems ◽  
Pose Variation

The face mask is an essential sanitaryware in daily lives growing during the pandemic period and is a big threat to current face recognition systems. The masks destroy a lot of details in a large area of face and it makes it difficult to recognize them even for humans. The evaluation report shows the difficulty well when recognizing masked faces. Rapid development and breakthrough of deep learning in the recent past have witnessed most promising results from face recognition algorithms. But they fail to perform far from satisfactory levels in the unconstrained environment during the challenges such as varying lighting conditions, low resolution, facial expressions, pose variation and occlusions. Facial occlusions are considered one of the most intractable problems. Especially when the occlusion occupies a large region of the face because it destroys lots of official features.

scholarly journals A novel framework for face recognition using robust local representation–based classification

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983608
Author(s):  
Aihua Yu ◽  
Gang Li ◽  
Beiping Hou ◽  
Hongan Wang ◽  
Gaoya Zhou
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recall Rate ◽  
Data Sets ◽  
Computation Complexity ◽  
Face Images ◽  
In The Wild ◽  
Recognition Systems

Face recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face recognition. To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via an optimized projection matrix. The recognition is carried out using an improved robust sparse coding algorithm. Such an algorithm is expected to avoid the overfitting problem. The open-universe test on labeled faces in the wild data sets shows that the recognition rate of the proposed system can reach 95% with a recall rate of 80%, which is best among those representation-based classification face recognition systems.

Local Linear Regression on Hybrid Eigenfaces for Pose Invariant Face Recognition

2012 ◽  
Vol 2 (2) ◽  
pp. 48-58 ◽  
Author(s):  
Ajay Jaiswal ◽  
Nitin Kumar ◽  
R. K. Agrawal
Keyword(s):  
Face Recognition ◽  
Linear Regression ◽  
Classification Accuracy ◽  
Local Linear Regression ◽  
New Approach ◽  
Local Linear ◽  
The Face ◽  
Recognition Systems ◽  
Pose Variation ◽  
Gallery Image

Pose variation leads to significant decline in the performance of the face recognition systems. In this paper, the authors propose a new approach HLLR, based on conjunction of hybrid-eigenfaces and local linear regression (LLR), to perform face recognition across pose. In this approach, LLR on hybrid-eigenfaces is used to generate virtual views. These virtual views in frontal and non-frontal poses are obtained using frontal gallery image. The performance of the proposed approach is compared for classification accuracy with another efficient method based on global linear regression on hybrid eigenface (HGLR). They also investigate the effect of number of images used to construct hybrid-eigenfaces on classification accuracy. Experimental results on two well known publicly available face databases demonstrate the effectiveness of the proposed approach. The suitability of proposed approach is also noticed when the number of available images is small.

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 Face Recognition Performance in Facing Pose Variation

2017 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Alexander Agung Santoso Gunawan ◽  
Reza A Prasetyo
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Principal Component ◽  
Threshold Value ◽  
Training Data ◽  
Face Images ◽  
Real World Applications ◽  
Controlled Environments ◽  
Pose Variation ◽  
Yaw Angle

There are many real world applications of face recognition which require good performance in uncontrolled environments such as social networking, and environment surveillance. However, many researches of face recognition are done in controlled situations. Compared to the controlled environments, face recognition in uncontrolled environments comprise more variation, for example in the pose, light intensity, and expression. Therefore, face recognition in uncontrolled conditions is more challenging than in controlled settings. In thisresearch, we would like to discuss handling pose variations in face recognition. We address the representation issue us ing multi-pose of face detection based on yaw angle movement of the head as extensions of the existing frontal face recognition by using Principal Component Analysis (PCA). Then, the matching issue is solved by using Euclidean distance. This combination is known as Eigenfaces method. The experiment is done with different yaw angles and different threshold values to get the optimal results. The experimental results show that: (i) the more pose variation of face images used as training data is, the better recognition results are, but it also increases the processing time, and (ii) the lower threshold value is, the harder it recognizes a face image, but it also increases the accuracy.

scholarly journals Assessing the Performance of DWT-PCA/SVD Face Recognition Algorithm under Multiple Constraints

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Joseph Agyapong Mensah ◽  
Louis Asiedu ◽  
Felix O. Mettle ◽  
Samuel Iddi
Keyword(s):  
Face Recognition ◽  
Pairwise Comparison ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Multiple Constraints ◽  
Comparison Test ◽  
Face Images ◽  
Significant Difference ◽  
Left And Right ◽  
Recognition Systems

Many architectures of face recognition modules have been developed to tackle the challenges posed by varying environmental constraints such as illumination, occlusions, pose, and expressions. These recognition systems have mainly focused on a single constraint at a time and have achieved remarkable successes. However, the presence of multiple constraints may deteriorate the performance of these face recognition systems. In this study, we assessed the performance of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transform (DWT-PCA/SVD) for preprocessing face recognition algorithm on multiple constraints (partially occluded face images acquired with varying expressions). Numerical evaluation of the study algorithm gave reasonably average recognition rates of 77.31% and 76.85% for left and right reconstructed face images with varying expressions, respectively. A statistically significant difference was established between the average recognition distance of the left and right reconstructed face images acquired with varying expressions using pairwise comparison test. The post hoc analysis using the Bonferroni simultaneous confidence interval revealed that the significant difference established through the pairwise comparison test was mainly due to the sad expressions. Although the performance of the DWT-PCA/SVD algorithm declined as compared to its performance on single constraints, the algorithm attained appreciable performance level under multiple constraints. The DWT-PCA/SVD recognition algorithm performs reasonably well for recognition when partial occlusion with varying expressions is the underlying constraint.

scholarly journals Facial UV map completion for pose-invariant face recognition: a novel adversarial approach based on coupled attention residual UNets

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
In Seop Na ◽  
Chung Tran ◽  
Dung Nguyen ◽  
Sang Dinh
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Generative Model ◽  
Superior Performance ◽  
3D Mesh ◽  
Testing Phase ◽  
Face Images ◽  
In The Wild ◽  
Pose Variation

Abstract Pose-invariant face recognition refers to the problem of identifying or verifying a person by analyzing face images captured from different poses. This problem is challenging due to the large variation of pose, illumination and facial expression. A promising approach to deal with pose variation is to fulfill incomplete UV maps extracted from in-the-wild faces, then attach the completed UV map to a fitted 3D mesh and finally generate different 2D faces of arbitrary poses. The synthesized faces increase the pose variation for training deep face recognition models and reduce the pose discrepancy during the testing phase. In this paper, we propose a novel generative model called Attention ResCUNet-GAN to improve the UV map completion. We enhance the original UV-GAN by using a couple of U-Nets. Particularly, the skip connections within each U-Net are boosted by attention gates. Meanwhile, the features from two U-Nets are fused with trainable scalar weights. The experiments on the popular benchmarks, including Multi-PIE, LFW, CPLWF and CFP datasets, show that the proposed method yields superior performance compared to other existing methods.

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