scholarly journals Assessing the Effect of Data Augmentation on Occluded Frontal Faces Using DWT-PCA/SVD Recognition Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Louis Asiedu ◽  
Joseph Agyapong Mensah ◽  
Francis Ayiah-Mensah ◽  
Felix O. Mettle
Keyword(s):  
Wavelet Transforms ◽  
Data Augmentation ◽  
Crime Control ◽  
Recognition Rate ◽  
Bilateral Symmetry ◽  
Recognition Algorithm ◽  
Discrete Wavelet ◽  
Human Computer Interactions ◽  
Face Images ◽  
Average Recognition Rate

The drift towards face-based recognition systems can be attributed to recent advances in supportive technology and emerging areas of application including voting systems, access control, human-computer interactions, entertainments, and crime control. Despite the obvious advantages of such systems being less intrusive and requiring minimal cooperation of subjects, the performances of their underlying recognition algorithms are challenged by the quality of face images, usually acquired from uncontrolled environments with poor illuminations, varying head poses, ageing, facial expressions, and occlusions. Although several researchers have leveraged on the property of bilateral symmetry to reconstruct half-occluded face images, their approach becomes deficient in the presence of random occlusions. In this paper, we harnessed the benefits of the multiple imputation by the chained equation technique and image denoising using Discrete Wavelet Transforms (DWTs) to reconstruct degraded face images with random missing pixels. Numerical evaluation of the study algorithm gave a perfect (100%) average recognition rate each for recognition of occluded and augmented face images. The study also revealed that the average recognition rate for the augmented face images (75.5811) was significantly lower than the average recognition rate (430.7153) of the occluded face images. MICE augmentation is recommended as a suitable data enhancement mechanism for imputing missing data/pixel of occluded face images.

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.

An Improved Face Recognition Algorithm Based on SIFT and LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1999-2004 ◽  
Author(s):  
Huai Ming Yang ◽  
Jin Guang Sun
Keyword(s):  
Recognition Rate ◽  
Recognition Algorithm ◽  
Image Feature ◽  
Local Similarity ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Face Images ◽  
Calculation Results ◽  
Scale Invariant Feature ◽  
Global And Local

A new face image feature extraction and recognition algorithm based on Scale Invariant Feature Transform (SIFT) and Local Linary Patterns (LBP) is proposed in this paper. Firstly, a set of keypoints are extracted from images by using the SIFT algorithm; Secondly, each keypoint is described by LBP patterns; Finally, a combination of the global and local similarity is adopted to calculate the matching results for face images. Calculation results show that the algorithm can reduce the matching dimension of feature points, improve the recognition rate and perspective; it has nice robustness against the interferences such as rotation, lighting and expression.

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.

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 Application of Random Forest and Hidden Markov Models for Automatic and Fast Classification of Power Quality Signals

2020 ◽  
Vol 9 (4) ◽  
pp. 2856-2862
Keyword(s):  
Wavelet Transform ◽  
Random Forest ◽  
Wavelet Transforms ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Second Generation Wavelet Transform ◽  
Quality Signals

In this paper, wavelet transform, namely the maximal overlap discrete Wavelet Transform (MODWT) and the second generation Wavelet Transform (SGWT) have been implemented. These wavelet transforms are applied to get selected features of the signals. Features are used as inputs to two types of classifiers namely, Hidden Markov Model (HMM) classifiers and the Random Forest (RF) classifier in the both absence and presence of Noise to evaluate the efficiency. The classification accuracy (CA) calculated using these classifiers clearly shows that the RF classifiers is a better classifier then the HMM classifier as it possess higher recognition rate at all levels of noise along with the pure PQ signals. Another important property of RF classifier is the proper classification of large number of class of both slow and the fast disturbances.

scholarly journals Face Recognition Use Local Image Dataset and Correlation Technique

2021 ◽  
Vol 5 (2) ◽  
pp. 26-31
Author(s):  
Dana Faiq Abd
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Wavelet Transforms ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Correlation Technique ◽  
Final Decision ◽  
Accuracy Rate ◽  
Public Datasets ◽  
Local Image

Face recognition is an extreme topic in security field which identifies humans through physiological or behavioral biometric characteristics. Face recognition can also identify the human almost in a precise detection; one of the primary problems in face recognition is the accurate recognition rate. Local datasets use for implementing this research rather than using public datasets. Midian filter uses to remove noise and identify errors, also obtains a good accuracy rate without modifying image quality. In addition, filter processing applies to modify and progress images and the discrete wavelet transforms algorithm uses as feature extraction. Many steps are applied in this approach such as image acquisition, converting images into gray scale, cropping the image, and then passing to the feature extraction. In order to get the final decision about the indicated face, some required steps are used in the comparison. The results show the accuracy of 91% of the recognition rate through the human face.

scholarly journals A New Face Image Recognition Algorithm Based on Cerebellum-Basal Ganglia Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Shoujun Tang ◽  
Mohammad Shabaz
Keyword(s):  
Face Recognition ◽  
Basal Ganglia ◽  
Recognition Rate ◽  
Great Accuracy ◽  
Recognition Algorithm ◽  
Image Features ◽  
Face Image ◽  
Geometric Feature ◽  
Face Images ◽  
High Recognition Accuracy

Face recognition is one of the popular areas of research in the field of computer vision. It is mainly used for identification and security system. One of the major challenges in face recognition is identification under numerous illumination environments by changing the direction of light or modifying the lighting magnitude. Exacting illumination invariant features is an effective approach to solve this problem. Conventional face recognition algorithms based on nonsubsampled contourlet transform (NSCT) and bionic mode are not capable enough to recognize the similar faces with great accuracy. Hence, in this paper, an attempt is made to propose an enhanced cerebellum-basal ganglia mechanism (CBGM) for face recognition. The integral projection and geometric feature assortment method are used to acquire the facial image features. The cognition model is deployed which is based on the cerebellum-basal ganglia mechanism and is applied for extraction of features from the face image to achieve greater accuracy for recognition of face images. The experimental results reveal that the enhanced CBGM algorithm can effectively recognize face images with greater accuracy. The recognition rate of 100 AR face images has been found to be 96.9%. The high recognition accuracy rate has been achieved by the proposed CBGM technique.

scholarly journals An Improved Face Recognition Using Neighborhood Defined Modular Phase Congruency Based Kernel PCA

2012 ◽  
pp. 288-296
Author(s):  
M.Lokeswara Reddy ◽  
P.Ramana Reddy
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Visual Image ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Kernel Pca ◽  
Phase Congruency ◽  
Face Images ◽  
Facial Recognition System

A face recognition algorithm based on NMPKPCA algorithm presented in this paper. The proposed algorithm when compared with conventional Principal component analysis (PCA) algorithms has an improved recognition Rate for face images with large variations in illumination, facial expressions. In this technique, first phase congruency features are extracted from the face image so that effects due to illumination variations are avoided by considering phase component of image. Then, face images are divided into small sub images and the kernel PCA approach is applied to each of these sub images. but, dividing into small or large modules creates some problems in recognition. So a special modulation called neighborhood defined modularization approach presented in this paper, so that effects due to facial variations are avoided. Then, kernel PCA has been applied to each module to extract features. So a feature extraction technique for improving recognition accuracy of a visual image based facial recognition system presented in this paper.

scholarly journals Signature Recognition Based on Discrete Wavelet Transform

2019 ◽  
Vol 3 (1) ◽  
pp. 19
Author(s):  
Sivana Salahadin Muhamad ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Feature Extraction ◽  
Wavelet Transforms ◽  
Recognition Rate ◽  
Personal Identification ◽  
Discrete Wavelet ◽  
Discrete Wavelet Transforms ◽  
Human Identity ◽  
Processing Stage ◽  
Signature Recognition ◽  
Developing Area

Personal identification is an actively developing area of research. Human signature is a vital biometric attribute which can be used to authenticate human identity. There are many approaches to recognize signature with a lot of researches. The aim of this research is to introduce an efficient approach for signature recognition. This approach starts with the process the acquired signatures and stores these signatures in the database to be ready for verification. The collection of signature data based on collecting samples of 10 people and 10 signatures for each person through traditional ink stamp method. These signatures are digitized to be ready for processing. Many steps are applied to the acquired images to perform the pre-processing stage. The proposed approach based on discrete wavelet transforms to extract significant features from each signature image. Pre-processing is applied at the beginning of this approach to avoid any unwanted noise. This approach consists of many steps: Data acquisition, pre-processing, signature registration, and feature extraction. High recognition rate results (100%) are obtained through applying this approach.

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.

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