scholarly journals Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera

2016 ◽  
Vol 6 (6) ◽  
pp. 2818
Author(s):  
Edy Winarno ◽  
Agus Harjoko ◽  
Aniati Murni Arymurthy ◽  
Edi Winarko
Keyword(s):  
Face Recognition ◽  
Stereo Vision ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Images ◽  
Face Recognition System ◽  
Proposed Model ◽  
The Face ◽  
Left And Right ◽  
Recognition Technique

<p>The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).</p>

Face Recognition Based on Symmetrical Half-Join Method using Stereo Vision Camera

2016 ◽  
Vol 6 (6) ◽  
pp. 2818 ◽  
Author(s):  
Edy Winarno ◽  
Agus Harjoko ◽  
Aniati Murni Arymurthy ◽  
Edi Winarko
Keyword(s):  
Face Recognition ◽  
Stereo Vision ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Images ◽  
Face Recognition System ◽  
Proposed Model ◽  
The Face ◽  
Left And Right ◽  
Recognition Technique

<p>The main problem in face recognition system based on half-face pattern is how to anticipate poses and illuminance variations to improve recognition rate. To solve this problem, we can use two lenses on stereo vision camera in face recognition system. Stereo vision camera has left and right lenses that can be used to produce a 2D image of each lens. Stereo vision camera in face recognition has capability to produce two of 2D face images with a different angle. Both angle of the face image will produce a detailed image of the face and better lighting levels on each of the left and right lenses. In this study, we proposed a face recognition technique, using 2 lens on a stereo vision camera namely symmetrical half-join. Symmetrical half-join is a method of normalizing the image of the face detection on each of the left and right lenses in stereo vision camera, then cropping and merging at each image. Tests on face recognition rate based on the variety of poses and variations in illumination shows that the symmetrical half-join method is able to provide a high accuracy of face recognition and can anticipate variations in given pose and illumination variations. The proposed model is able to produce 86% -97% recognition rate on a variety of poses and variations in angles between 0 °- 22.5 °. The variation of illuminance measured using a lux meter can result in 90% -100% recognition rate for the category of at least dim lighting levels (above 10 lux).</p>

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.

scholarly journals Online Training for Face Recognition System Using Improved PCA

2011 ◽  
Vol 2 (2) ◽  
pp. 1303
Author(s):  
Widodo Budiharto
Keyword(s):  
Face Recognition ◽  
Stereo Vision ◽  
Indoor Environment ◽  
Online Training ◽  
Recognition System ◽  
Service Robot ◽  
Challenging Problem ◽  
Face Database ◽  
Face Images ◽  
Face Recognition System

The variation in illumination is one of the main challenging problem for face recognition. It has been proven that in face recognition, differences caused by illumination variations are more significant than differences between individuals. Recognizing face reliably across changes in pose and illumination using PCA has proved to be a much harder problem because eigenfaces method comparing the intensity of the pixel. To solve this problem, this research proposes an online face recognition system using improved PCA for a service robot in indoor environment based on stereo vision. Tested images are improved by generating random values for varying the intensity of face images. A program for online training is also developed where the tested images are captured real-time from camera. Varying illumination in tested images will increase the accuracy using ITS face database which its accuracy is 95.5 %, higher than ATT face database’s as 95.4% and Indian face database’s as 72%. The results from this experiment are still evaluated to be improved in the future.

scholarly journals Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

2017 ◽  
Vol 13 (1) ◽  
pp. 104-113
Author(s):  
Yaqeen Mezaal
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Input Image ◽  
Birth Date ◽  
Image Preprocessing ◽  
Face Recognition System ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Local Average

Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images.

scholarly journals A Modified CNN-Based Face Recognition System

2021 ◽  
Vol 12 (2) ◽  
pp. 1-20
Author(s):  
Jayanthi Raghavan ◽  
Majid Ahmadi
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Softmax Classifier ◽  
Face Recognition System ◽  
Proposed Model ◽  
The Face ◽  
Batch Sizes ◽  
Deep Cnn ◽  
Fully Connected ◽  
Improved Accuracy

In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%.

scholarly journals Preprocessing Techniques to Improve CNN based Face Recognition System

2021 ◽  
Author(s):  
Jayanthi Raghavan ◽  
Majid Ahmadi
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Softmax Classifier ◽  
Face Recognition System ◽  
Proposed Model ◽  
The Face ◽  
Batch Sizes ◽  
Deep Cnn ◽  
Fully Connected ◽  
Improved Accuracy

In this work, deep CNN based model have been suggested for face recognition. CNN is employed to extract unique facial features and softmax classifier is applied to classify facial images in a fully connected layer of CNN. The experiments conducted in Extended YALE B and FERET databases for smaller batch sizes and low value of learning rate, showed that the proposed model has improved the face recognition accuracy. Accuracy rates of up to 96.2% is achieved using the proposed model in Extended Yale B database. To improve the accuracy rate further, preprocessing techniques like SQI, HE, LTISN, GIC and DoG are applied to the CNN model. After the application of preprocessing techniques, the improved accuracy of 99.8% is achieved with deep CNN model for the YALE B Extended Database. In FERET Database with frontal face, before the application of preprocessing techniques, CNN model yields the maximum accuracy of 71.4%. After applying the above-mentioned preprocessing techniques, the accuracy is improved to 76.3%.

scholarly journals Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

2017 ◽  
Vol 13 (1) ◽  
pp. 104-113
Author(s):  
Yaqeen Mezaal
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Input Image ◽  
Birth Date ◽  
Image Preprocessing ◽  
Face Recognition System ◽  
Proposed Model ◽  
Occurrence Matrix ◽  
Local Average

Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images.

scholarly journals Implications of Deep Learning-Based Methods for Face Recognition in Online Examination System

2019 ◽  
Vol 8 (3) ◽  
pp. 1204-1208
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Recognition System ◽  
Light Illumination ◽  
Examination System ◽  
Learning Methods ◽  
Face Recognition System ◽  
Proposed Model ◽  
Online Examination ◽  
The Face

In the recent era, the importance of surveillance-related applications is increasing rapidly. In such applications, Face Recognition is becoming an emerging, fast-growing research field in the security authentication systems. Face recognition becomes one of the biometric techniques for identifying individuals face in digital images or in the stored image. It has various applications in biometrics, military, video surveillance and so on. In an earlier age, face recognition techniques implemented using a traditional approach like holistic based, hybrid and feature-based. In the traditional system, there are a number of issues like light illumination, occlusion problem, different facial expressions, and poses of the particular individual. These factors are affecting the accuracy and efficiency of the face recognition system. Nowadays there is an advancement in the technology and methods which are used in the face recognition system. The new methods and techniques of face recognition are devised by deep learning methods. The research focuses on a proposed model developed by using some Deep Learning methods and frameworks for face recognition. This model plays an important role in the authentication of an individual in the online examination system in educational institutes. Multi-level authentication is used for authenticating individual and to have crosschecked over throughout the examination period. The Deep Learning methods and frameworks overcome the issues raised in face recognition by traditional methods. This proposed model used for the authentication of an individual in educational institutes where online examinations are conducted.

scholarly journals Identification and Recognition of facial images

2020 ◽  
pp. 69-76
Author(s):  
Mallika Kohli ◽  
Vasundra Wazir ◽  
Parul Sharma ◽  
Pawanesh Abrol
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Main Task ◽  
Face Recognition System ◽  
The Face ◽  
Almost All ◽  
Facial Images

Face detection is the power to identify a face and recognition is the ability to recognize whose face it is by means of facial characteristics. Face is multivariate and requires a lot of mathematical summation. Almost all imperative applications use a face recognition system. There are many methods that have been already proposed which provides low recognition rate. Hence, the main task of research is to develop a face recognition system with higher recognition capability and better accuracy. This paper proposes Face recognition system by combining two techniques Viola Jones and Principal Component Analysis. An approach of Eigen faces is employed in Principle Component Analysis(PCA). The face recognition system is implemented in MATLAB.

Research on Face Recognition Technology of Real-Time Video High Performance Similarity

2015 ◽  
Vol 713-715 ◽  
pp. 2160-2164
Author(s):  
Zhao Nan Yang ◽  
Shu Zhang
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Real Time ◽  
High Performance ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Recognition System ◽  
Image Preprocessing ◽  
Face Images ◽  
Face Recognition System

A new similarity measurement standard is proposed, namely background similarity matching. Learning algorithm based on kernel function is utilized in the method for feature extraction and classification of face image. Meanwhile, a real-time video face recognition method is proposed, image binary algorithm in similarity calculation is introduced, and a video face recognition system is designed and implemented [1-2]. The system is provided with a camera to obtain face images, and face recognition is realized through image preprocessing, face detection and positioning, feature extraction, feature learning and matching. Design, image preprocessing, feature positioning and extraction, face recognition and other major technologies of face recognition systems are introduced in details. Lookup mode from top down is improved, thereby improving lookup accuracy and speed [3-4]. The experimental results showed that the method has high recognition rate. Higher recognition rate still can be obtained even for limited change images of face images and face gesture with slightly uneven illumination. Meanwhile, training speed and recognition speed of the method are very fast, thereby fully meeting real-time requirements of face recognition system [5]. The system has certain face recognition function and can well recognize front faces.

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