The Application of Face Recognition Based on OpenCV

2011 ◽  
Vol 403-408 ◽  
pp. 2350-2353
Author(s):  
Su Li
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Biometric Recognition ◽  
The Core ◽  
Face Recognition System ◽  
Learning Functions ◽  
Key Technologies ◽  
Pca Algorithm ◽  
Mathematical Operations

Face recognition is a significant method, which is one of the biometric recognition. A face recognition system consists of two key technologies, namely, face detection and face recognition. In order to achieve two key technologies, Haar-Like feature and AdsBoost algorithm can be used to achieve face detection module. And PCA algorithm can be used to achieve face recognition module. For achieve application more quickly and efficiently, the core of the system develops with OpenCV. And the main use is its image processing, mathematical operations, and machine learning functions.

Research and Implementation of Face Detection Based on OpenCV

2014 ◽  
Vol 971-973 ◽  
pp. 1710-1713
Author(s):  
Wen Huan Wu ◽  
Ying Jun Zhao ◽  
Yong Fei Che
Keyword(s):  
Face Recognition ◽  
Detailed Analysis ◽  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Accuracy Rate ◽  
Face Recognition System ◽  
The Face

Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

FACE DETECTION AND RECOGNITION USING MAXIMUM LIKELIHOOD CLASSIFIERS ON GABOR GRAPHS

2009 ◽  
Vol 23 (03) ◽  
pp. 433-461 ◽  
Author(s):  
MANUEL GÜNTHER ◽  
ROLF P. WÜRTZ
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Face Detection ◽  
Recognition System ◽  
Similarity Functions ◽  
Expression Variation ◽  
Face Recognition System ◽  
Face Detection And Recognition ◽  
Ml Estimator ◽  
Detection And Recognition

We present an integrated face recognition system that combines a Maximum Likelihood (ML) estimator with Gabor graphs for face detection under varying scale and in-plane rotation and matching as well as a Bayesian intrapersonal/extrapersonal classifier (BIC) on graph similarities for face recognition. We have tested a variety of similarity functions and achieved verification rates (at FAR 0.1%) of 90.5% on expression-variation and 95.8% on size-varying frontal images within the CAS-PEAL database. Performing Experiment 1 of FRGC ver2.0, the method achieved a verification rate of 72%.

scholarly journals Face recognition attendance system using Local Binary Pattern (LBP)

2019 ◽  
Vol 8 (1) ◽  
pp. 239-245 ◽  
Author(s):  
Shamsul J. Elias ◽  
Shahirah Mohamed Hatim ◽  
Nur Anisah Hassan ◽  
Lily Marlia Abd Latif ◽  
R. Badlishah Ahmad ◽  
...  
Keyword(s):  
Image Processing ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Automated System ◽  
Student Attendance ◽  
Recognition Method ◽  
Face Recognition System ◽  
The Face

Attendance is important for university students. However, generic way of taking attendance in universities may include various problems. Hence, a face recognition system for attendance taking is one way to combat the problem. This paper will present an automated system that will automatically saves student’s attendance into the database using face recognition method. The paper will elaborate on student attendance system, image processing, face detection and face recognition. The face detection part will be done by using viola-jones algorithm method while the face recognition part will be carried on by using local binary pattern (LBP) method. The system will ensure that the attendance taking process will be faster and more accurate.

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.

FACE RECOGNITION SYSTEM FOR ELECTRONIC MEDICAL RECORD TO ACCESS OUT-PATIENT INFORMATION

2016 ◽  
Vol 78 (6-3) ◽  
Author(s):  
Maribelle Dequilla Pabiania ◽  
Krizchel Ann P. Santos ◽  
Maureen M. Villa-Real ◽  
Jervin Angelo N. Villareal
Keyword(s):  
Face Recognition ◽  
Medical Record ◽  
Face Detection ◽  
Patient Information ◽  
Medical Information ◽  
Recognition System ◽  
Easy Access ◽  
Medical Professionals ◽  
Face Recognition System ◽  
User Acceptability

The study aimed to create an enhanced monitoring and management system of patients’ medical records for hospital clinics to provide easy identification of patients and give easy access to the doctors and nurses regarding patient’s medical information. A face recognition system to access patient information was created by means of hardware and software integration. The hardware consisted mainly of a webcam for capturing the image of the patient’s face. The webcam, together with the servos were connected to a gizDuino v4.0 microcontroller to allow the camera to track the face. An interface and software program using C# (.Net Framework 4) with the use of Viola-Jones algorithm code sources for face detection, Eigen-faces sources for face recognition, and Arduino IDE to program the microcontroller was developed. The content of the medical record were based on the conducted survey answered by medical professionals and saved in MySQL database. With the use of five-point Likert scale, the user-acceptability of the system was tested by doctors, nurses, medical technologists, and other medical professionals. A good result was obtained where 8 out of 10 questions were rated as ‘strongly agreed’ and the other two were rated ‘agreed’ by the survey respondents. The test for face detection yielded a 100% result and out of 30 trials conducted for face recognition, 25 were recognized with its respective record. This indicates that the system is functional and of good quality.  

scholarly journals Challenges in Face Detection and Recognition techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 221-224
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Research Paper ◽  
Facial Features ◽  
Image Information ◽  
Public Crisis ◽  
Face Recognition System ◽  
Face Detection And Recognition ◽  
Detection And Recognition

Face Recognition System is popular topic in the biometric world .This system provide Features to detect the person’s face and identify on basis of existing records in database .The aim of this study is to described how to show various facial features of an image. Face Recognition system, based on Biometric AI, uniquely finds out a person by analyzing the person's facial textures and shape. In this paper, our aim is to study various face detect and recognition techniques such as Harr Like Feature Algorithm resulting to retort criminality and public crisis. Also, some facial recognition approaches PCA and LDA have been discussed in the research paper for abstracting the image information.

scholarly journals Attendance Posting Through Face Recognition

2019 ◽  
Vol 8 (4) ◽  
pp. 11652-11654
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Biometric Authentication ◽  
New Approach ◽  
Important Method ◽  
Security Issues ◽  
Detection Technology ◽  
Face Recognition System ◽  
The Face

Now a day’s face detection technology is widely used technique. It attracted attention for much valuable application in the market such as face recognition system. Biometric authentication is most important method in security system. Universally used Biometric fingerprint scanner can be bypassed quite easily. It can be broke easily. Biometric face recognition has been introduced to improve the security of a system. Methods such as Motion based and texture based are used for biometric face recognition. But these methods have less robustness and poor generalization ability. But apart from further security issues, this paper presents a new approach to make attendance of the student in class by the face recognition. Now a day’s attendance system is usually done manually or by the biometric fingerprint. Those are mistaken and tedious techniques. So this technique records the student’s participation in classroom consequently and provide facility for teachers for obtaining the data of the student effectively using log to check in and out time

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