iJADE AUTHENTICATOR — AN INTELLIGENT MULTIAGENT BASED FACIAL AUTHENTICATION SYSTEM

2002 ◽  
Vol 16 (04) ◽  
pp. 481-500 ◽  
Author(s):  
RAYMOND S. T. LEE
Keyword(s):  
User Authentication ◽  
Recognition Rate ◽  
Recognition System ◽  
User Friendliness ◽  
Development Environment ◽  
Agent Based ◽  
Face Recognition System ◽  
Shopping Experience ◽  
Correct Recognition Rate ◽  
Agent Development

In modern consumer e-shopping environments, customer authentication is a critical process for confirming the identity of the customer. Traditional authentication techniques that rely on the customers to proactively identify themselves (using various schemes) can affect the user-friendliness of the e-shopping experience, and therefore reduce the customers' preference for such facilities. In this paper, we propose an innovative intelligent multiagent-based environment, called iJADE (intelligent Java Agent Development Environment) to provide an intelligent agent-based platform in the e-commerce environment. Contemporary agent development platforms are focused on the autonomy and mobility of the agents, whereas iJADE provides an intelligent layer (known as the "conscious layer") to implement various AI (artificial intelligence) functionalities in order to produce "smart" agents. From an implementation perspective, we introduce an innovative e-shopping authentication scheme called the "iJADE Authenticator", which is an invariant face recognition system that uses intelligent mobile agents. This system can provide fully automatic, mobile and reliable user authentication. More importantly, the authentication process can be carried out without the users necessarily being aware of it. Experimental results are presented for a database of 1020 tested face images obtained under conditions of widely varying facial expressions, viewing perspectives and image sizes. An overall average correct recognition rate of over 90% is attained.

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 Real time Implementation of Face Recognition System on Raspberry Pi

2018 ◽  
Vol 7 (2.17) ◽  
pp. 85
Author(s):  
K Raju ◽  
Dr Y.Srinivasa Rao
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Rate ◽  
Cost Effective ◽  
Recognition System ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Effective System ◽  
Face Detection And Recognition ◽  
Improved Accuracy

Face Recognition is the ability to find and detect a person by their facial attributes. Face is a multi dimensional and thus requires a considerable measure of scientific calculations. Face recognition system is very useful and important for security, law authorization applications, client confirmation and so forth. Hence there is a need for an efficient and cost effective system. There are numerous techniques that are as of now proposed with low Recognition rate and high false alarm rate. Hence the major task of the research is to develop face recognition system with improved accuracy and improved recognition time. Our objective is to implementing Raspberry Pi based face recognition system using conventional face detection and recognition techniques such as A Haar cascade classifier is trained for detection and Local Binary Pattern (LBP) as a feature extraction technique. With the use of the Raspberry Pi kit, we go for influencing the framework with less cost and simple to use, with high performance. 

scholarly journals Performance Improvement of Face Recognition System using Selective Local Feature Vectors

2013 ◽  
Vol 10 (2) ◽  
pp. 1330-1338
Author(s):  
Vasudha S ◽  
Neelamma K. Patil ◽  
Dr. Lokesh R. Boregowda
Keyword(s):  
Face Recognition ◽  
Performance Improvement ◽  
Recognition Rate ◽  
Recognition System ◽  
Image Splicing ◽  
Feature Vectors ◽  
Face Recognition System ◽  
Prime Concern ◽  
Wide Range ◽  
Recognition Systems

Face recognition is one of the important applications of image processing and it has gained significant attention in wide range of law enforcement areas in which security is of prime concern. Although the existing automated machine recognition systems have certain level of maturity but their accomplishments are limited due to real time challenges. Face recognition systems are impressively sensitive to appearance variations due to lighting, expression and aging. The major metric in modeling the performance of a face recognition system is its accuracy of recognition. This paper proposes a novel method which improves the recognition accuracy as well as avoids face datasets being tampered through image splicing techniques. Proposed method uses a non-statistical procedure which avoids training step for face samples thereby avoiding generalizability problem which is caused due to statistical learning procedure. This proposed method performs well with images with partial occlusion and images with lighting variations as the local patch of the face is divided into several different patches. The performance improvement is shown considerably high in terms of recognition rate and storage space by storing train images in compressed domain and selecting significant features from superset of feature vectors for actual recognition.

Face Recognition System using Discrete Cosine Transform combined with MLP and RBF Neural Networks

2012 ◽  
Vol 4 (4) ◽  
pp. 11-35 ◽  
Author(s):  
Fatma Zohra Chelali ◽  
Amar Djeradi
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Discrete Cosine Transform ◽  
Recognition Rate ◽  
Recognition System ◽  
Rbf Neural Networks ◽  
Cosine Transform ◽  
Linear Discriminant ◽  
Face Recognition System ◽  
Dct Coefficients

Proposed is an efficient face recognition algorithm using the discrete cosine transform DCT Technique for reducing dimensionality and image parameterization. These DCT coefficients are examined by a MLP (Multi-Layer Perceptron) and radial basis function RBF neural networks. Their purpose is to present a face recognition system that is a combination of discrete cosine transform (DCT) algorithm with a MLP and RBF neural networks. Neural networks have been widely applied in pattern recognition for the reason that neural-networks-based classifiers can incorporate both statistical and structural information and achieve better performance than the simple minimum distance classifiers. The authors demonstrate experimentally that when DCT coefficients are fed into a back propagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. Comparison with other statistical methods like Principal component Analysis (PCA) and Linear Discriminant Analysis (LDA) is presented. Their face recognition system is tested on the computer vision science research projects and the ORL database.

scholarly journals Face Recognition Using MLP and RBF Neural Network with Gabor and Discrete Wavelet Transform Characterization: A Comparative Study

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Fatma Zohra Chelali ◽  
Amar Djeradi
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Rbf Neural Network ◽  
Recognition Rate ◽  
Recognition System ◽  
Extraction Methods ◽  
Discrete Wavelet ◽  
Face Representation ◽  
Face Recognition System ◽  
Mlp Classifier

Face recognition has received a great attention from a lot of researchers in computer vision, pattern recognition, and human machine computer interfaces in recent years. Designing a face recognition system is a complex task due to the wide variety of illumination, pose, and facial expression. A lot of approaches have been developed to find the optimal space in which face feature descriptors are well distinguished and separated. Face representation using Gabor features and discrete wavelet has attracted considerable attention in computer vision and image processing. We describe in this paper a face recognition system using artificial neural networks like multilayer perceptron (MLP) and radial basis function (RBF) where Gabor and discrete wavelet based feature extraction methods are proposed for the extraction of features from facial images using two facial databases: the ORL and computer vision. Good recognition rate was obtained using Gabor and DWT parameterization with MLP classifier applied for computer vision dataset.

Canonical Correlation Analysis on Fusion of Global and Local Features of Face Recognition

2014 ◽  
Vol 926-930 ◽  
pp. 3598-3603
Author(s):  
Xiao Xiong ◽  
Guo Fa Hao ◽  
Peng Zhong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Local Features ◽  
Global Features ◽  
Features Fusion ◽  
Local Characteristics ◽  
Face Recognition System ◽  
Global And Local ◽  
Effective Integration

Face recognition belongs to the important content of the biometric identification, which is a important method in research of image processing and pattern recognition. It can effectively overcome the traditional authentication defects Through the facial recognition technology. At present, face recognition under ideal state research made some achievements, but the changes in light, shade, expression, posture changes the interference factors such as face recognition is still exist many problems. For this, put forward the integration of global and local features of face recognition research. Practice has proved that through the effective integration of global features and local characteristics, build based on global features and local features fusion face recognition system, can improve the recognition rate of face recognition, face recognition application benefit.

3D Angle Searching System with PSO for Face Recognition

2013 ◽  
Vol 284-287 ◽  
pp. 2950-2954
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu ◽  
Meng Shian Shih
Keyword(s):  
Particle Swarm Optimization ◽  
Face Recognition ◽  
Open Source ◽  
3D Modeling ◽  
Recognition Rate ◽  
Computation Time ◽  
Recognition System ◽  
Experimental Results ◽  
Swarm Optimization ◽  
Face Recognition System

Conventional 2D face recognition methods often struggle when a subject's head is turned even slightly to the side. In this study, a face recognition system based on 3D head modeling that is able to tolerate facial rotation angles was constructed by leveraging the Open source graphic library (OpenGL) framework. To minimize the extensive angle searching time that often occurs in conventional 3D modeling, Particle Swarm Optimization (PSO) was used to determine the correct facial angle in 3D. This reduced the angle computation time to 6 seconds, which is significantly faster than other methods. Experimental results showed that successful ID recognition can be achieved with a high recognition rate of 90%.

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