scholarly journals A FACIAL RECOGNITION USING OPEN COMPUTER VISION

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr.C K Gomathy ◽  
T. suneel ◽  
Y.Jeeevan Kumar Reddy
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Functional Space ◽  
Recognition System ◽  
Statistical Technique ◽  
Exciting Field ◽  
Two Dimensional Image ◽  
Face Recognition System ◽  
The Face ◽  
Amount Of Knowledge

The Face recognition and image or video recognition are popular research topics in biometric technology. Real-time face recognition is an exciting field and a rapidly evolving issue. Key component analysis (PCA) may be a statistical technique collectively called correlational analysis . The goal of PCA is to scale back the massive amount of knowledge storage to the dimensions of the functional space required to render the face recognition system. The wide one-dimensional pixel vector generated from the two-dimensional image of the face and therefore the basic elements of the spatial function are designed for face recognition using PCA. this is often the projection of your own space. Sufficient space is decided by the brand. specialise in the eigenvectors of the covariance matrix of the fingerprint image collection. i'm building a camera-based real-time face recognition system and installing an algorithm. Use OpenCV, Haar Cascade, Eigen face, Fisher Face, LBPH and Python for program development.

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.

scholarly journals Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system

2021 ◽  
Vol 10 (2) ◽  
pp. 1105-1113
Author(s):  
Mohd Suhairi Md Suhaimin ◽  
Mohd Hanafi Ahmad Hijazi ◽  
Chung Seng Kheau ◽  
Chin Kim On
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Recognition System ◽  
Report Generation ◽  
Face Recognition System ◽  
The Face ◽  
Common Face ◽  
Future Work ◽  
Pattern Histogram

Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated.

scholarly journals High-Capacity Real-Time Face Retrieval Recognition Algorithm Based on Task Scheduling Model for the Treatment Area of Hospital

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yi Zhou ◽  
Weili Xia
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Real Time ◽  
Task Scheduling ◽  
High Capacity ◽  
Recognition System ◽  
Treatment Area ◽  
Scheduling Model ◽  
Face Recognition System ◽  
The Face

This paper presents an in-depth study of face detection, face feature extraction, and face classification from three important components of a high-capacity face recognition system for the treatment area of hospital and a study of a high-capacity real-time face retrieval and recognition algorithm for the treatment area of hospital based on a task scheduling model. Considering the real-time nature of our system, our face feature extraction network is modeled by DeepID, and the network is slightly improved by introducing a central loss verification signal to train a DeepID-like network model using central loss and use it to extract face features. To further investigate and optimize the schedulability analysis problem of the directed graph real-time task model, this paper proposes a rigorous and approximate response time analysis method for the directed graph real-time task model with an arbitrary time frame. Based on the theoretical results of the greatly additive algebra, it is shown that the coherent qualifying function is linearly periodic, i.e., the function can be represented by a finite nonperiodic part and an infinitely repeated periodic part, thus calculating the coherent qualifying function independent of the magnitude of the interval time. The algorithm for high-capacity real-time face retrieval and recognition in the treatment area of hospital based on the task scheduling model is further investigated, and a face database is established by using the PCA dimensionality reduction technique. Based on the internal architecture of the processor, image preprocessing and IP core packaging are implemented, and the hardware engineering of the high-capacity real-time face recognition system for hospital visits is built using the IP-based design concept. The performance tests of the face detection model and feature extraction network show that the face detection model has a significant reduction in false-positive rate, better fitting of border regression, and improved time performance. The face feature extraction network has no overfitting, and the features are highly discriminative with small feature extraction time consumption. The high-capacity real-time face recognition system for the treatment area of hospital combined with the optimized directed graph task scheduling model can approach 25 fps, which meets the real-time requirements, and the face recognition rate surpasses that of real people. It realizes the intelligence, self-help, and autonomy of medical services and satisfies the medical needs of users in all aspects.

scholarly journals Face Recognition Door Unlock System

2019 ◽  
Vol 8 (11S) ◽  
pp. 1133-1139
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Facial Recognition ◽  
First Grade ◽  
Recognition System ◽  
Video Frame ◽  
Raspberry Pi ◽  
Face Recognition System ◽  
Essential Components ◽  
The Face

Face recognition system is widely used for human identification particularly for security functions. The project deals with the look and implementation of secure automatic door unlockby using Raspberry Pi. Web camera for capturing the images from the video frame is operated and controlled by raspberry pi using Open CVPython library to train and store human faces for recognition. In this project we are using Raspberry Pi as face recognition module to capture human images and it will compare with stored data base images. If it matches with authorized user then system allows to supply power to electromagnetic lock to create magnetic field for unlocking the door. The need for facial recognition system that is fast and accurate is continuously increasing which can detect intruders and restricts all unauthorized users from highly secured areas and aids in minimizing human error. Face recognition is one of the most Secured System than the biometric pattern recognition technique which is used in a large spectrum of applications.The time and accuracy factor is considered about the major problem which specifies the performance of automatic face recognition in real time environments. Various solutions have been proposed using multicore systems. By considering present challenge, this provides the complete architectural design and proposes an analysis for a real time face recognition. Thus, the image extracted and allowed to match with the database pictures. If the images are matched, the door unlocks mechanically. the planning of the face recognition system exploitation Raspberry pi will create the smaller, lighter and with lower power consumption, therefore it's a lot of convenient than the PC-based face recognition system. Principle element analysis LBPH (Local Binary Pattern Histogram) algorithmic program is employed for the face recognition and detection method. Then acknowledgement are send through Zigbee module from transmitter to receiver. If image isn't detected in database then it'll ask for manual four digit pin for unlocking the door.The developed theme is affordable, fast, and extremely reliable and provides enough flexibility to suits any environment of various systems. Problem Statement:In theworld of emerging technology, security became an essential component in day to day life. Information theft, lack of security and violation of privacy etc. are the essential components which are needed to be protected. Using smart secure systems for door lock and unlocking became popular nowadays. This is system is being adapted by many countries and first grade countries such as USA, Japan etc., already makes use of this system. This system provides either a facial recognition security feature or a keypad is provided to enter the passcode which unlocks the door. Although, it provides security to the doors, it also has somelimitations and drawbacks: Firstly, if the system mainly uses a facial recognition module, there might be a slight chance that sometimes the face may not be detected and hence the door cannot be unlocked. Secondly, if the system uses a

Real-time optically processed face-recognition system based on arbitrary moiré contours

1996 ◽  
Author(s):  
Rafael A. Andrade ◽  
Bernard R. Gilbert III ◽  
Donald W. Dawson ◽  
Chris L. Hart ◽  
Samuel P. Kozaitis ◽  
...  
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Recognition System ◽  
Face Recognition System

scholarly journals Real-time Triplet Loss Embedding Face Recognition for Authentication Student Attendance Records System Framework

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Hady Pranoto ◽  
Oktaria Kusumawardani
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Success Factors ◽  
Recognition System ◽  
Expression Recognition ◽  
Student Attendance ◽  
Face Recognition System ◽  
Triplet Loss ◽  
Education Process

The number of times students attend lectures has been identified as one of many success factors in the learning process in many studies. We proposed a framework of the student attendance system by using face recognition as authentication. Triplet loss embedding in FaceNet is suitable for face recognition systems because the architecture has high accuracy, quite lightweight, and easy to implement in the real-time face recognition system. In our research, triplet loss embedding shows good performance in terms of the ability to recognize faces. It can also be used for real-time face recognition for the authentication process in the attendance recording system that uses RFID. In our study, the performance for face recognition using k-NN and SVM classification methods achieved results of 96.2 +/- 0.1% and 95.2 +/- 0.1% accordingly. Attendance recording systems using face recognition as an authentication process will increase student attendance in lectures. The system should be difficult to be faked; the system will validate the user or student using RFID cards using facial biometric marks. Finally, students will always be present in lectures, which in turn will improve the quality of the existing education process. The outcome can be changed in the future by using a high-resolution camera. A face recognition system with facial expression recognition can be added to improve the authentication process. For better results, users are required to perform an expression instructed by face recognition using a database and the YOLO process.

scholarly journals Integarted Minimum Cost Sub-Block Matching Distance based Face Recognition using Internet of Things

2019 ◽  
Vol 8 (11) ◽  
pp. 117-122
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Minimum Cost ◽  
Recognition System ◽  
Block Matching ◽  
Face Region ◽  
Input Face ◽  
Face Recognition System ◽  
The Face ◽  
Fuzzy C Means Clustering

Now a days one of the critical factors that affects the recognition performance of any face recognition system is partial occlusion. The paper addresses face recognition in the presence of sunglasses and scarf occlusion. The face recognition approach that we proposed, detects the face region that is not occluded and then uses this region to obtain the face recognition. To segment the occluded and non-occluded parts, adaptive Fuzzy C-Means Clustering is used and for recognition Minimum Cost Sub-Block Matching Distance(MCSBMD) are used. The input face image is divided in to number of sub blocks and each block is checked if occlusion present or not and only from non-occluded blocks MWLBP features are extracted and are used for classification. Experiment results shows our method is giving promising results when compared to the other conventional techniques.

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.

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