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 Recognition of Reconstructed Frontal Face Images Using FFT-PCA/SVD Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Louis Asiedu ◽  
Felix O. Mettle ◽  
Joseph A. Mensah
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Environmental Constraints ◽  
Biometric Data ◽  
Face Images ◽  
The Face ◽  
Left And Right ◽  
Value Decomposition ◽  
User Friendly

Face recognition has gained prominence among the various biometric-based methods (such as fingerprint and iris) due to its noninvasive characteristics. Modern face recognition modules/algorithms have been successful in many application areas (access control, entertainment/leisure, security system based on biometric data, and user-friendly human-machine interfaces). In spite of these achievements, the performance of current face recognition algorithms/modules is still inhibited by varying environmental constraints such as occlusions, expressions, varying poses, illumination, and ageing. This study assessed the performance of Principal Component Analysis with singular value decomposition using Fast Fourier Transform (FFT-PCA/SVD) for preprocessing the face recognition algorithm on left and right reconstructed face images. The study found that average recognition rates for the FFT-PCA/SVD algorithm were 95% and 90% when the left and right reconstructed face images are used as test images, respectively. The result of the paired sample t-test revealed that the average recognition distances for the left and right reconstructed face images are not significantly different when FFT-PCA/SVD is used for recognition. FFT-PCA/SVD is recommended as a viable algorithm for recognition of left and right reconstructed face images.

scholarly journals Unrestricted Face Recognition Algorithm Based on Transfer Learning on Self-Pickup Cabinet

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhixue Liang
Keyword(s):  
Face Recognition ◽  
Transfer Learning ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Face Image ◽  
Delivery Device ◽  
Face Images ◽  
Region Extraction ◽  
The Face ◽  
Enhancement Algorithm

In the contactless delivery scenario, the self-pickup cabinet is an important terminal delivery device, and face recognition is one of the efficient ways to achieve contactless access express delivery. In order to effectively recognize face images under unrestricted environments, an unrestricted face recognition algorithm based on transfer learning is proposed in this study. First, the region extraction network of the faster RCNN algorithm is improved to improve the recognition speed of the algorithm. Then, the first transfer learning is applied between the large ImageNet dataset and the face image dataset under restricted conditions. The second transfer learning is applied between face image under restricted conditions and unrestricted face image datasets. Finally, the unrestricted face image is processed by the image enhancement algorithm to increase its similarity with the restricted face image, so that the second transfer learning can be carried out effectively. Experimental results show that the proposed algorithm has better recognition rate and recognition speed on the CASIA-WebFace dataset, FLW dataset, and MegaFace dataset.

scholarly journals Evaluation of the DWT-PCA/SVD Recognition Algorithm on Reconstructed Frontal Face Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Louis Asiedu ◽  
Bernard O. Essah ◽  
Samuel Iddi ◽  
K. Doku-Amponsah ◽  
Felix O. Mettle
Keyword(s):  
Principal Component ◽  
Bilateral Symmetry ◽  
Recognition Algorithm ◽  
Face Image ◽  
Discrete Wavelet ◽  
Partial Occlusion ◽  
Face Images ◽  
Numerical Assessment ◽  
The Face ◽  
Value Decomposition

The face is the second most important biometric part of the human body, next to the finger print. Recognition of face image with partial occlusion (half image) is an intractable exercise as occlusions affect the performance of the recognition module. To this end, occluded images are sometimes reconstructed or completed with some imputation mechanism before recognition. This study assessed the performance of the principal component analysis and singular value decomposition algorithm using discrete wavelet transform (DWT-PCA/SVD) as preprocessing mechanism on the reconstructed face image database. The reconstruction of the half face images was done leveraging on the property of bilateral symmetry of frontal faces. Numerical assessment of the performance of the adopted recognition algorithm gave average recognition rates of 95% and 75% when left and right reconstructed face images were used for recognition, respectively. It was evident from the statistical assessment that the DWT-PCA/SVD algorithm gives relatively lower average recognition distance for the left reconstructed face images. DWT-PCA/SVD is therefore recommended as a suitable algorithm for recognizing face images under partial occlusion (half face images). The algorithm performs relatively better on left reconstructed face images.

scholarly journals Three-dimensional face recognition using variance-based registration and subject-specific descriptors

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141985171 ◽  
Author(s):  
Naeem Iqbal Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Nouman Ali ◽  
Anzar Mahmood ◽  
...  
Keyword(s):  
Face Recognition ◽  
Three Dimensional ◽  
Recognition Algorithm ◽  
Superior Performance ◽  
Data Sets ◽  
Face Images ◽  
Subject Specific ◽  
The Face ◽  
The Subject ◽  
Depth Curves

Face recognition underpins numerous applications; however, the task is still challenging mainly due to the variability of facial pose appearance. The existing methods show competitive performance but they are still short of what is needed. This article presents an effective three-dimensional pose invariant face recognition approach based on subject-specific descriptors. This results in state-of-the-art performance and delivers competitive accuracies. In our method, the face images are registered by transforming their acquisition pose into frontal view using three-dimensional variance of the facial data. The face recognition algorithm is initialized by detecting iso-depth curves in a coordinate plane perpendicular to the subject gaze direction. In this plane, discriminating keypoints are detected on the iso-depth curves of the facial manifold to define subject-specific descriptors using subject-specific regions. Importantly, the proposed descriptors employ Kernel Fisher Analysis-based features leading to the face recognition process. The proposed approach classifies unseen faces by pooling performance figures obtained from underlying classification algorithms. On the challenging data sets, FRGC v2.0 and GavabDB, our method obtains face recognition accuracies of 99.8% and 100% yielding superior performance compared to the existing methods.

Face Recognition Algorithm Based on 3D Point Cloud Acquired by Mixed Image Sensor

2019 ◽  
Vol 23 (2) ◽  
pp. 366-369
Author(s):  
Tang-Tang Yi ◽  
Keyword(s):  
Face Recognition ◽  
Point Cloud ◽  
Recognition Accuracy ◽  
Image Sensor ◽  
Recognition Algorithm ◽  
Face Image ◽  
3D Point Cloud ◽  
3D Face ◽  
Face Images ◽  
The Face

In order to solve the problem of low recognition accuracy in recognition of 3D face images collected by traditional sensors, a face recognition algorithm for 3D point cloud collected by mixed image sensors is proposed. The algorithm first uses the 3D wheelbase to expand the face image edge. According to the 3D wheelbase, the noise of extended image is detected, and median filtering is used to eliminate the detected noise. Secondly, the priority of the boundary pixels to recognize the face image in the denoising image recognition process is determined, and the key parts such as the illuminance line are analyzed, so that the recognition of the 3D point cloud face image is completed. Experiments show that the proposed algorithm improves the recognition accuracy of 3D face images, which recognition time is lower than that of the traditional algorithm by about 4 times, and the recognition efficiency is high.

Neural Network-Based Approach for Face Recognition Across Varying Pose

2015 ◽  
Vol 29 (08) ◽  
pp. 1556015 ◽  
Author(s):  
Seyed Omid Shahdi ◽  
S. A. R. Abu-Bakar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Local Information ◽  
Discrete Wavelet ◽  
Face Images ◽  
The Face ◽  
The Relationship

At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

scholarly journals An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jun Huang ◽  
Kehua Su ◽  
Jamal El-Den ◽  
Tao Hu ◽  
Junlong Li
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Feature Space ◽  
Recognition Algorithm ◽  
Linear Discriminant ◽  
Face Images ◽  
Sample Set ◽  
Multilinear Principal Component Analysis

We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA). Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while theKnearest neighbor (KNN) is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.

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