scholarly journals Hybrid Framework for a Robust Face Recognition System Using EVB_CNN

2021 ◽  
Vol 23 (3) ◽  
pp. 43-57
Author(s):  
Tamilselvi M. ◽  
S. Karthikeyan
Keyword(s):  
Face Recognition ◽  
Computation Time ◽  
Recognition System ◽  
Traditional Methods ◽  
Data Set ◽  
Face Recognition System ◽  
Eigen Value ◽  
Frame Work ◽  
Robust Face Recognition ◽  
Fully Connected

Recognition of the human face is becoming an ingenious technology that enhancing its strategy gradually by finding its applications in a wide variety of fields including security and surveillance. The traditional methods that are in practise for face recognition are not adequate in producing good accuracy due to two main reasons. The first one is the pictures are affected by various uncontrolled situations such as illumination, blur, and pose, and the second one is struggling in an efficient recognition when dealing with a large number of samples. There is need for an effective face recognition as a part of life in the automated environment. The traditional methods are lagging with some parameters. To overcome the aforementioned issues, a new methodology is implemented. This methodology is a hybrid frame work combined with Eigen value-based convolutional neural networks (EVB_CNN). The EVB_CNN is designed in such a way that the significant features are extracted and classified by the softmax function and fully connected layer, respectively. The experimental analysis is carried out with AR data set and ORL data set that shows enhancement in accuracy with significant reduction in computation time with images taken over specific uncontrolled environments.

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 Face Recognition System for Indoor Applications Based on Video with the LNMF and NMFsc Methods

2020 ◽  
Vol 55 (6) ◽  
Author(s):  
Della Gressinda Wahana ◽  
Bambang Hidayat ◽  
Suci Aulia ◽  
Sugondo Hadiyoso
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Face Detection ◽  
Matrix Factorization ◽  
Computation Time ◽  
Principal Component ◽  
Recognition System ◽  
Face Recognition System ◽  
Average Accuracy ◽  
Non Negative Matrix Factorization

Face detection and face recognition are among the most important research topics in computer vision, as many applications use faces as objects of biometric technology. One of the main issues in applying face recognition is recording the attendance of active participants in a room. The challenge is that recognition through video with multiple object conditions in one frame may be difficult to perform. The Principal Component Analysis method is commonly used in face detection. Principal Component Analysis still has shortcomings: the accuracy decreases when it is applied to large datasets and performs slowly in real-time applications. Therefore, this study simulates a face recognition system installed in a room based on video recordings using Non-negative Matrix Factorization suppressed carrier and Local Non-negative Matrix Factorization methods. Data acquisition is obtained by capturing video in classrooms with a resolution of 640 x 480 pixels in RGB, .avi format, video frame rate of 30 fps, and video duration of ±10 seconds. The proposed system can perform face recognition in which the average accuracy value of the Local Non-negative Matrix Factorization method is 71.61% with a computation time of 152,634 seconds. By contrast, the average accuracy value of the Non-negative Matrix Factorization suppressed carrier method is 86.76% with a computation time of 467,785 seconds. The proposed system is expected to be used for simultaneously finding and identifying faces in real time.

scholarly journals A Novel Genetic Convolutional Neuro Multi-Fuzzy Techniques for Newborn Face Recognition

2021 ◽  
Vol 12 (6) ◽  
pp. 1037-1046
Author(s):  
T. Arul Raj, Et. al.
Keyword(s):  
Fuzzy Logic ◽  
Face Recognition ◽  
Recognition System ◽  
Biometric Recognition ◽  
Face Recognition System ◽  
Neuro Fuzzy ◽  
The Face ◽  
Face Datasets ◽  
Fuzzy Techniques ◽  
Fully Connected

Advances in technology have made life simpler in today's society by supplying us with a variety of emerging demands lacking By assessing the progressive stability of biometric recognition accuracy for newborns, biometric recognition can be used to recognize missing newborns and prevent them from being switched in higher-level hospitals.. Recognizing and authenticating newborns is a major problem in many hospitals. The face recognition system does an outstanding job of identifying and authenticating the newborn. To answer these concerns, create a face recognition device for newborns. The proposed approach improves picture consistency on a newborn's face. Our objectives are to propose a genetic, convolutional neural network, and fuzzy logic-based automated framework for newborn face recognition. As a paradigm GCNMF is suggested for real-world newborn face recognition. Convolutional, pooling, and fully-connected layers, as well as a Neuro Fuzzy layer, form the Inherited Convolutional Neuro Multi-Fuzzy. The model employs hereditary, convolutional neural networks, and fuzzy logic to deal with ambiguity and imprecision in the input configuration representation. The efficacy and outcomes of the recommended method are then analyzed using newborn face datasets and the Genetic Convolutional Neuro Multi-Fuzzy (GCNMF) Approach.

scholarly journals FACE RECOGNITION SYSTEM FOR MACHINE READABLE TRAVEL DOCUMENTS

2014 ◽  
pp. 32-38
Author(s):  
Sergey Tulyakov ◽  
Rauf Kh. Sadykhov
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Computation Time ◽  
Recognition System ◽  
Detection And Identification ◽  
Face Recognition System ◽  
High Processing Speed ◽  
Speed Up ◽  
Machine Readable ◽  
High Processing

This paper presents an upright frontal face recognition system, aimed to recognize faces on machine readable travel documents (MRTD). The system is able to handle large image databases with high processing speed and low detection and identification errors. In order to achieve high accuracy eyes are detected in the most probable regions, which narrows search area and therefore reduces computation time. Recognition is performed with the use of eigenface approach. The paper introduces eigenface basis ranking measure, which is helpful in challenging task of creating the basis for recognition purposes. To speed up identification process we split the database into males and females using high - performance AdaBoost classifier. At the end of the paper the results of the tests in speed and accuracy are given.

scholarly journals Using Median-LBPH Algorithm for Real-Time Face Recognition System

2021 ◽  
Vol 9 (11) ◽  
pp. 1497-1503
Author(s):  
Lohith Raj S N
Keyword(s):  
Face Recognition ◽  
Law Enforcement ◽  
Real Time ◽  
Web Application ◽  
Computation Time ◽  
Recognition System ◽  
Text Messages ◽  
Law Enforcement Agencies ◽  
Face Expression ◽  
Face Recognition System

Abstract: The LBPH algorithm is used ubiquitously for Face Recognition applications in modern times because of its simplicity of implementation, despite providing high accuracy and less computation time. However, in conditions of varied illumination, face expression and angles at which face images are captured, its confidence is decreased. We propose a slightly modified algorithm that considers the median of the neighbourhood pixels rather than the pixel itself to overcome this issue. This algorithm is called Median-LBPH. The grey value of every pixel is replaced by the median of all the neighbourhood pixel values. Then the features are extracted, and a histogram representing the original image is saved in the model. This model, in turn, can be used to compare with histograms obtained from the faces in real-time footage to find a potential match. This algorithm is used in an end-to-end face recognition system, a web application prototype for Law Enforcement Agencies to maintain a central criminal database shared and accessed across various departments. A live surveillance system is added as part of this novel application so that whenever an already registered criminal appears live on surveillance cameras, a notification will be received, and personnel appropriate Law Enforcement authorities will receive e-mail and text messages through a secured channel. Keywords: Face Recognition, Median-Local Binary Pattern Histogram (MLBPH), Haar Cascade, Adaboost, Neighbourhood Median

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

2008 ◽  
pp. 188-211
Author(s):  
Ting Shan ◽  
Abbas Bigdeli ◽  
Brian C. Lovell ◽  
Shaokang Chen
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
Principal Component ◽  
Recognition System ◽  
Active Appearance Models ◽  
Face Recognition System ◽  
The Face ◽  
The Embedded System ◽  
Computationally Intensive ◽  
Robust Face Recognition

In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

Sign in / Sign up

Export Citation Format

Share Document