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.

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 Formal Implementation of a Performance Evaluation Model for the Face Recognition System

2008 ◽  
Vol 2008 ◽  
pp. 1-10 ◽  
Author(s):  
Yong-Nyuo Shin ◽  
Jason Kim ◽  
Yong-Jun Lee ◽  
Woochang Shin ◽  
Jin-Young Choi
Keyword(s):  
Performance Evaluation ◽  
Face Recognition ◽  
Evaluation System ◽  
Performance Test ◽  
Evaluation Model ◽  
Recognition System ◽  
Performance Evaluation Model ◽  
Face Recognition System ◽  
Recognition Systems ◽  
A Performance

Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.

scholarly journals Individual identification of Japanese macaques (Macaca fuscata) using a face recognition system and a limited number of learning images

2020 ◽  
Author(s):  
Yosuke Otani ◽  
Hitoshi Ogawa
Keyword(s):  
Face Recognition ◽  
Macaca Fuscata ◽  
Field Research ◽  
Recognition System ◽  
Japanese Macaques ◽  
Individual Identification ◽  
System Construction ◽  
Face Recognition System ◽  
Recognition Systems ◽  
High Degree

AbstractIndividual identification is an important technique in animal research that requires researcher training and specialized skillsets. Face recognition systems using artificial intelligence (AI) deep learning have been put into practical use to identify in humans and animals, but a large number of annotated learning images are required for system construction. In wildlife research cases, it is difficult to prepare a large amount of learning images, which may be why systems using AI have not been widely used in field research. To investigate the development of a system that identifies individuals using a small number of learning images, we constructed a system to identify individual Japanese macaques (Macaca fuscata yakui) with a low error rate from an average of 20 images per individual. The characteristics of this system were augmentation of data, simultaneous determination by four individual identification models and identification from a majority of five frames to ensure reliability. This technology has a high degree of utility for various stakeholders and it is expected that it will advance the development of individual identification systems by AI that can be widely used in field research.

scholarly journals Railway Access Control System using Face Recognition

2020 ◽  
Vol 9 (3) ◽  
pp. 444-448
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Modern World ◽  
Qr Code ◽  
Quick Response ◽  
Code System ◽  
Railway Station ◽  
Face Recognition System ◽  
Access Control System ◽  
Recognition Systems

Nowadays booking tickets and getting inside a railway station is adifficult task. Manual checking becomes a burden and time consuming. Also as everything is getting digitized in this modern world introduce face recognition and Quick Response (QR) code system for entry helps in passenger convenience.Face recognition is a method of identifying or verifying the identity of an individual using their face. Face recognition systems can be used to identify people in photos, video, or in real-time.So this system focuses on passengers’ convenience through allowing them to book tickets online and by introducing face recognition system and QR code system for entry to a railway station.This system helps inidentifying people who try to travel without buying tickets and also helps toapprehend the blacklisted person which increases security in the railway station. Online booking is one of the convenient ways tobook the ticket. This system also provides the convenience to passenger by issuing the digital ticket in the form of QR code thus avoiding any fuss due to the loss of the physical ticket.

scholarly journals Using Local Features in Face Recognition Systems

2018 ◽  
pp. 131-134
Author(s):  
Yildiz Aydin ◽  
Funda Akar
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Local Features ◽  
Support Vector ◽  
Global Feature ◽  
Affine Transformations ◽  
Face Recognition System ◽  
Long Time ◽  
Recognition Systems ◽  
The Many

Among the many applications in the field of computer vision, face recognition systems; is a subject that has been studied extensively and has been working for a long time. In general, the success of facial recognition systems, which consist of feature extraction and classifier steps, depends not only on the classifier but also on the features used. In a face recognition system, the feature selection is to obtain distinctive features for recognition of different facial images of interest. For this purpose, SIFT, SURF and SIFT + SURF features, which are unchanging features to scaling and affine transformations, are used in this study. In addition, to be able to compare with these local features, the HOG feature which is a global feature, also has been added to the study. Classification was performed using support vector machine. Experimental results show that local features are more successful than the global feature HOG.

scholarly journals Performance of Face Recognition System Using Gradient Laplacian Operators and New Features Extraction Method Based on Linear Regression Slope

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Abdulbasit Alazzawi ◽  
Osman N. Ucan ◽  
Oguz Bayat
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Linear Regression ◽  
Edge Detection ◽  
Extraction Method ◽  
Principal Component ◽  
Recognition System ◽  
Detection Techniques ◽  
Face Recognition System ◽  
Recognition Systems

Recent research proves that face recognition systems can achieve high-quality results even in non-ideal environments. Edge detection techniques and feature extraction methods are popular mechanisms used in face recognition systems. Edge detection can be used to construct the face map in the image efficiently, in which feature extraction techniques generate the most suitable features that can identify human faces. In this study, we present a new and efficient face recognition system that uses various gradient-and Laplacian-based operators with a new feature extraction method. Different edge detection operators are exploited to obtain the best image edges. The new and robust method based on the slope of the linear regression, called SLP, uses the estimated face lines in its feature extraction step. Artificial neural network (ANN) is used as a classifier. To determine the best scheme that gives the best performance, we test combinations of various techniques such as (Sobel filter (SF), SLP/principal component analysis (PCA), ANN), (Prewitt filter(PF), SLP/PCA, ANN), (Roberts filter (RF), SLP/PCA, ANN), (zero cross filter (ZF), SLP/PCA, ANN), (Laplacian of Gaussian filter (LG), SLP/PCA, ANN), and (Canny filter(CF), SLP/PCA, ANN). The BIO ID dataset is used in the training and testing phases for the proposed face recognition system combinations. Experimental results indicate that the proposed schemes achieve satisfactory results with high-accuracy classification. Notably, the combinations of (SF, SLP, ANN) and (ZF, SLP, ANN) gain the best results and outperform all the other algorithm combinations.

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.

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