scholarly journals An Optimized Face Recognition System Using Cuckoo Search

2019 ◽  
Vol 28 (2) ◽  
pp. 321-332 ◽  
Author(s):  
Preeti Malhotra ◽  
Dinesh Kumar
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Cuckoo Search ◽  
Recognition System ◽  
Extraction Methods ◽  
Optimization Techniques ◽  
High Rate ◽  
Face Recognition System ◽  
Recognition Systems ◽  
System Feature

Abstract The development of an effective and efficient face recognition system has always been a challenging task for researchers. In a face recognition system, feature selection is one of the most vital processes to achieve maximum accuracy by removing irrelevant and superfluous data. Many optimization techniques, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization, etc., have been implemented in face recognition systems mainly based on two feature extraction methods: discrete cosine transform (DCT) and principal component analysis (PCA). In this research, a nature-inspired well-known algorithm, namely cuckoo search, has been implemented for face recognition. Further, a hybrid method consisting of DCT and PCA is applied to extract the various features by which recognition can be made with a high rate of accuracy. To validate the proposed methodology, the results are also compared with the existing methodologies, such as PSO, differential evolution, and GA.

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.

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 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.

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 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 Real-Time Attendance and Feedback System

2021 ◽  
pp. 1135-1139
Author(s):  
Piyush Manish Sonar ◽  
Aniket Nitin Chaudhari ◽  
Mehul Deepak Sethi ◽  
Tejaswini Sanjay Gadakh
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Feedback System ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Student Attendance ◽  
Feedback Form ◽  
Face Recognition System ◽  
User Friendly ◽  
Facial Images

Face is the representation of one’s identity. Hence, we have proposed an automated student attendance system based on face recognition. Face recognition system is very useful in life applications especially for attendance system. In our proposed approach, firstly, video framing is performed by activating the camera through a user-friendly interface. In the pre-processing stage, scaling of the size of images is performed, if necessary, in order to prevent loss of information. In face recognition stage, enhanced local binary pattern (LBP) and principal component analysis (PCA) is applied correspondingly in order to extract the features from facial images. Another way of marking the attendance is fingerprint recognition. To mark the attendance students simply have to give the fingerprint impression in fingerprint scanner module. Finally, the attendance of the recognized student will be marked and saved in the excel file. The student who is not registered will also be able to register on the spot and notification will be given if students sign in more than once. Whenever seminar is completed then a link is sent on email. It includes the information in terms of feedback. When student fills the feedback form then analysis of overall session is done.

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.

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