scholarly journals An effective face recognition method using guided image filter and convolutional neural network

2021 ◽  
Vol 23 (3) ◽  
pp. 1699
Author(s):  
Yallamandaiah S. ◽  
Purnachand N.
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition Method ◽  
Image Filter ◽  
Guided Image Filter ◽  
Face Region ◽  
The Face ◽  
Recognition Systems

<p>In the area of computer vision, face recognition is a challenging task because of the pose, facial expression, and illumination variations. The performance of face recognition systems reduces in an unconstrained environment. In this work, a new face recognition approach is proposed using a guided image filter, and a convolutional neural network (CNN). The guided image filter is a smoothing operator and performs well near the edges. Initially, the ViolaJones algorithm is used to detect the face region and then smoothened by a guided image filter. Later the proposed CNN is used to extract the features and recognize the faces. The experiments were performed on face databases like ORL, JAFFE, and YALE and attained a recognition rate of 98.33%, 99.53%, and 98.65% respectively. The experimental results show that the suggested face recognition method attains good results than some of the state-of-the-art techniques.</p>

Face Recognition Based on Wavelet Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 216-219
Author(s):  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
The Face ◽  
Simulation Testing

It's well known that the technology of human face recognition has become a hot topicin pattern recognition field. Though a lot of progress has been made by many researchersthese years, many key problems still have to be solved in order to popularize the application of face recognition because of the complexity of face recognition. The background, development and main methods of face recognition are introducedfirstly in this paper, then a face recognition method which is based on wavelet transform,KL transform and BP neural networks is used in the paper.Here the face feature extraction includes wavelet transform and KL transform.Moreover,the recognition classifier is BP neural networks.The simulation testing in the paper holds good recognition rate.

scholarly journals Face Recognition System Against Adversarial Attack Using Convolutional Neural Network

2021 ◽  
Vol 18 (1) ◽  
pp. 1-8
Author(s):  
Ansam Kadhim ◽  
Salah Al-Darraji
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Face Recognition System ◽  
The Face ◽  
Recognition Systems ◽  
Projected Gradient Descent ◽  
Employee Attendance ◽  
Sign Method

Face recognition is the technology that verifies or recognizes faces from images, videos, or real-time streams. It can be used in security or employee attendance systems. Face recognition systems may encounter some attacks that reduce their ability to recognize faces properly. So, many noisy images mixed with original ones lead to confusion in the results. Various attacks that exploit this weakness affect the face recognition systems such as Fast Gradient Sign Method (FGSM), Deep Fool, and Projected Gradient Descent (PGD). This paper proposes a method to protect the face recognition system against these attacks by distorting images through different attacks, then training the recognition deep network model, specifically Convolutional Neural Network (CNN), using the original and distorted images. Diverse experiments have been conducted using combinations of original and distorted images to test the effectiveness of the system. The system showed an accuracy of 93% using FGSM attack, 97% using deep fool, and 95% using PGD.

scholarly journals Face Image Set Recognition Based On Improved HOG-NMF and Convolutional Neural Networks

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Lixiu Hao ◽  
Weiwei Yu
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Adverse Effects ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Weight Coefficient ◽  
Face Image ◽  
Segmented Images ◽  
The Face ◽  
Image Set

Objective Face recognition can be affected by unfavorable factors such as illumination, posture and expression, but the face image set is a collection of people’s various angles, different illuminations and even different expressions, which can effectively reduce these adverse effects and get higher face recognition rate. In order to make the face image set have higher recognition rate, a new method of combining face image set recognition is proposed, which combines an improved Histogram of Oriented Gradient (HOG) feature and Convolutional Neural Network (CNN). Method The method firstly segments the face images to be identified and performs HOG to extract features of the segmented images. Secondly, calculate the information entropy contained in each block as a weight coefficient of each block to form a new HOG features, and non-negative matrix factorization (NMF) is applied to reduce HOG features. Then the reduced-dimensional HOG features are modeled as image sets which keep your face details as much as possible. Finally, the modeled image sets are classified by using a convolutional neural network. Result The experimental results show that compared with the simple CNN method and the HOG-CNN method, the recognition rate of the method on the CMU PIE face set is increased by about 4%~10%. Conclusion The method proposed in this paper has more details of the face, overcomes the adverse effects, and improves the accuracy.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals Deep Learning- Based Surveillance System using Face Recognition

2020 ◽  
Vol 32 ◽  
pp. 03011
Author(s):  
Divya Kapil ◽  
Aishwarya Kamtam ◽  
Akhil Kedare ◽  
Smita Bharne
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Surveillance System ◽  
Surveillance Systems ◽  
Learning Method ◽  
Security Systems ◽  
The Face

Surveillance systems are used for the monitoring the activities directly or indirectly. Most of the surveillance system uses the face recognition techniques to monitor the activities. This system builds the automated contemporary biometric surveillance system based on deep learning. The application of the system can be used in various ways. The face prints of the persons will be stored inside the database with relevant statistics and does the face recognition. When any unknown face is recognized then alarm will ring so one can alert the security systems and in addition actions will be taken. The system learns changes while detecting faces automatically using deep learning and gain correct accuracy in face recognition. A deep learning method including Convolutional Neural Network (CNN) is having great significance in the area of image processing. This system can be applicable to monitor the activities for the housing society premises.

A Hybrid Approach For Automatic Licence Plate Recognition System

2020 ◽  
Vol 10 ◽  
Author(s):  
Nitin Sharma ◽  
Pawan Kumar Dahiya ◽  
Baldev Raj Marwah
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Traffic Monitoring ◽  
Support Vector ◽  
Network Support ◽  
Recognition Systems ◽  
Licence Plate Recognition

: Automatic licence plate recognition systems are used for various applications such as traffic monitoring, toll collection, car parking, law enforcement. In this paper, a convolutional neural network and support vector machine based automatic licence plate recognition system is proposed. Firstly, The characters extracts from the input image of vehicle. Then characters are segment and their features are extracts. The extracted features are classified using convolutional neural network and support vector machine for the final recognition of the licence plate. The obtained recognition rate by the hybridization of the convolutional neural network and the support vector machine is 96.5%. The recognition rate obtained for the proposed hybrid automatic licence plate system are compared with three other automatic licence plate systems based on neural network, support vector machine, and convolutional neural network. The proposed automatic licence plate recognition system perform better than the neural network, support vector machine, and convolutional nerural network based automatic licence plate recognition systems.

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.

POSE-EXPRESSION NORMALIZATION FOR FACE RECOGNITION USING CONNECTED COMPONENTS ANALYSIS

2006 ◽  
Vol 20 (06) ◽  
pp. 869-881
Author(s):  
JAE-YOUNG CHOI ◽  
TAEG-KEUN WHANGBO ◽  
YOUNG-GYU YANG ◽  
MURLIKRISHNA VISWANATHAN ◽  
NAK-BIN KIM
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Connected Components ◽  
Recognition Method ◽  
Facial Region ◽  
Detection Model ◽  
Recognition Systems ◽  
Components Analysis ◽  
Empirical Tests ◽  
Connected Components Analysis

Accurate measurement of poses and expressions can increase the efficiency of recognition systems by avoiding the recognition of spurious faces. This paper presents a novel and robust pose-expression invariant face recognition method in order to improve the existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the vector X-Y-Z of face using connected components analysis. Second, the input face is mapped by a deformable 3D facial model. Third, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector and action unit of expression. Finally, the damaged regions which occur during the process of normalization are reconstructed using PCA. Several empirical tests are used to validate the application of face detection model and the method for estimating facial poses and expression. In addition, the tests suggest that recognition rate is greatly boosted through the normalization of the poses and expression.

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