Face Detection and Recognition using Color Segmentation,Template Matching and Gabor Neural Network with Fuzzy System

2018 ◽  
Vol 6 (3) ◽  
pp. 29-38
Author(s):  
Mays Kareem Jabbar ◽  
Maab Alaa Hussain ◽  
Thaar A. Kareem
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Fuzzy System ◽  
Template Matching ◽  
Recognition System ◽  
Color Segmentation ◽  
Second Phase ◽  
Face Recognition System ◽  
The Face

Face recognition is the process of finding the face of one or more people in an image or even in a video. There are variety techniques for face recognition used in the researches. In this paper various algorithms for face recognition on mobile phones or other electronic device are applied. firstly the face detection should be implemented in any face recognition system. To get the face detection many algorithms like color segmentation, template matching etc are applicated. Then the second phase of the proposed algorithm is implemented by using neural network Gabor with fuzzy system. The algorithm has been represented using MATLAB and then implemented it on the device. While implementing the proposed algorithm, a tradeoff between accuracy and computational complexity of the algorithm are made, because the face recognition system is implemented on a device with limited hardware capabilities

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 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 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

scholarly journals Design of Facial Recognition System Based on Visual Communication Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xuhui Fu
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Visual Communication ◽  
Facial Recognition ◽  
Recognition System ◽  
Image Capture ◽  
Hardware System ◽  
Face Recognition System ◽  
The Face ◽  
Facial Recognition System

At present, facial recognition technology is a very cutting-edge science and technology, and it has now become a very hot research branch. In this research, first, the thesis first summarized the research status of facial recognition technology and related technologies based on visual communication and then used the OpenCV open source vision library based on the design of the system architecture and the installed system hardware conditions. The face detection program and the image matching program are realized, and the complete face recognition system based on OpenCV is realized. The experimental results show that the hardware system built by the software can realize the image capture and online recognition. The applied objects are testers. In general, the OpenCV-based face recognition system for testers can reliably, stably, and quickly realize face detection and recognition in this situation. Facial recognition works well.

scholarly journals Face Recognition System Against Adversarial Attack Using Convolutional Neural Network

2021 ◽  
Vol 18 (1) ◽  
pp. 1-8
Author(s):  
Ansam Kadhim ◽  
Salah Al-Darraji
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
Face Recognition System ◽  
The Face ◽  
Recognition Systems ◽  
Projected Gradient Descent ◽  
Employee Attendance ◽  
Sign Method

Face recognition is the technology that verifies or recognizes faces from images, videos, or real-time streams. It can be used in security or employee attendance systems. Face recognition systems may encounter some attacks that reduce their ability to recognize faces properly. So, many noisy images mixed with original ones lead to confusion in the results. Various attacks that exploit this weakness affect the face recognition systems such as Fast Gradient Sign Method (FGSM), Deep Fool, and Projected Gradient Descent (PGD). This paper proposes a method to protect the face recognition system against these attacks by distorting images through different attacks, then training the recognition deep network model, specifically Convolutional Neural Network (CNN), using the original and distorted images. Diverse experiments have been conducted using combinations of original and distorted images to test the effectiveness of the system. The system showed an accuracy of 93% using FGSM attack, 97% using deep fool, and 95% using PGD.

Detection Face Parts in Image Using Neural Network Based on MATLAB

2021 ◽  
Vol 39 (1B) ◽  
pp. 159-164
Author(s):  
Shahad L. Galib ◽  
Fouad S. Tahir ◽  
Asma A. Abdulrahman
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Recognition System ◽  
Color Images ◽  
Surveillance Systems ◽  
Independent Components ◽  
New Methods ◽  
Matlab Program ◽  
Face Recognition System ◽  
The Face

Recently, face recognition system (FRS) is implemented in different applications including a range of vital services like airports and banking systems for security purposes. Therefore, deployed surveillance systems have been established which led to the urgent need to develop a vital face recognition system. In this work, a new algorithm was proposed for recognition of the face, personal and color images by training the convolutional neural network using the MATLAB program to build a new program for detection of the face, then building a separate program to discover the lips, nose, and eyes, New methods were explored to analyze the main and independent components to improve face detection, which is considered one of the important techniques in this work using neural networks and implementation through the MATLAB program.

scholarly journals WEBINAR STUDENT PRESENCE SYSTEM BASED ON REGIONAL CONVOLUTIONAL NEURAL NETWORK USING FACE RECOGNITION

2021 ◽  
Vol 2 (2) ◽  
pp. 109-118
Author(s):  
Akbar Trisnamulya Putra ◽  
Koredianto Usman ◽  
Sofia Saidah
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Recognition System ◽  
World Health ◽  
Face To Face ◽  
Face Recognition System ◽  
The Social ◽  
The Face ◽  
Health Organization

World health organization announce Covid-19 as a pandemic so On March 15th 2020, the social distancing has been established with working, learning, and praying from home. Webinar is one of the solutions so those activities still can be done face to face and conference-based. With webinar, users can interact each other in an online meeting from home. Student presence is part of a webinar. The purpose of this research is to design an accurate student presence with a face recognition system using R-CNN method. The object of this research is a human face with sufficient light, medium, and the face must be facing the camera. This research proposed for a webinar student presence system is using face recognition with Regional Convolutional Neural Network (R-CNN). With object detection and several scenarios used in this method, the webinar student presence system using R-CNN will be more accurate than the methods that have ever been used before. This research has done four scenarios to obtain the best parameters like 45 of total layers, test data of the whole dataset percentage as 10%, RMSProp as model op- timizer, and 0.0001 learning rate. With those parameters, it have resulted the best system performance including 99.6% accuration, 1 × 10-4 loss, 100% precision, 99% recall, and 99.5% F1 Score.

scholarly journals A Face Recognition Method in the Internet of Things for Security in Smart Recognition Places

2022 ◽  
Vol 10 (1) ◽  
pp. 687-690
Author(s):  
Prof. Kalpana Malpe
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Associate Degree ◽  
Human Life ◽  
Recognition System ◽  
Raspberry Pi ◽  
Living Things ◽  
Face Recognition System ◽  
The Face ◽  
Face Detection And Recognition

Abstract: In recent years, the safety constitutes the foremost necessary section of the human life. At this point, the price is that the greatest issue. This technique is incredibly helpful for reducing the price of watching the movement from outside. During this paper, a period of time recognition system is planned which will equip for handling pictures terribly quickly. The most objective of this paper is to safeguard home, workplace by recognizing individuals. The face is that the foremost distinctivea part of human’s body. So, it will replicate several emotions of associate degree Expression. A few years past, humans were mistreatment the non-living things like good cards, plastic cards, PINS, tokens and keys for authentication, and to urge grant access in restricted areas like ISRO, National Aeronautics and Space Administration and DRDO. The most necessary options of the face image are Eyes, Nose and mouth. Face detection and recognition system is simpler, cheaper, a lot of accurate, process. The system under two categories one is face detection and face recognition. Throughout this case, among the paper, the Raspberry Pi single-board computer is also a heart of the embedded face recognition system. Keywords: Raspberry Pi, Face recognition system

scholarly journals The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances

2022 ◽  
Author(s):  
Hang Du ◽  
Hailin Shi ◽  
Dan Zeng ◽  
Xiao-Ping Zhang ◽  
Tao Mei
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Face Alignment ◽  
Face Representation ◽  
Learning Techniques ◽  
Face Recognition System ◽  
The Face ◽  
End To End

Face recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face recognition has made remarkable progress and been widely used in many real-world applications. Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. To achieve this, a typical end-to-end system is built with three key elements: face detection, face alignment, and face representation. The face detection locates faces in the image or frame. Then, the face alignment is proceeded to calibrate the faces to the canonical view and crop them with a normalized pixel size. Finally, in the stage of face representation, the discriminative features are extracted from the aligned face for recognition. Nowadays, all of the three elements are fulfilled by the technique of deep convolutional neural network. In this survey article, we present a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved the capability of them. To start with, we present an overview of the end-to-end deep face recognition. Then, we review the advance of each element, respectively, covering many aspects such as the to-date algorithm designs, evaluation metrics, datasets, performance comparison, existing challenges, and promising directions for future research. Also, we provide a detailed discussion about the effect of each element on its subsequent elements and the holistic system. Through this survey, we wish to bring contributions in two aspects: first, readers can conveniently identify the methods which are quite strong-baseline style in the subcategory for further exploration; second, one can also employ suitable methods for establishing a state-of-the-art end-to-end face recognition system from scratch.

scholarly journals PENGGUNAAN MODEL HIDDEN MARKOV DAN METODE NEURAL NETWORK SEBAGAI PENERAPAN TEKNOLOGI PENGENALAN WAJAH

2019 ◽  
Vol 2 (1) ◽  
pp. 13
Author(s):  
Aria Hendrawan ◽  
Basworo Ardi Pramono ◽  
Whisnumurti Adhiwibowo
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition System ◽  
Hybrid Approaches ◽  
Image Characteristics ◽  
Face Recognition System ◽  
The Face ◽  
Sequential Images

The human face recognition system is one of the fields that is quite developed at this time, where applications can be applied in the field of security (security system) such as permission to access room, surveillance (surveillance), as well as the search for individual identities in the police database. The face recognition approach aims to detect faces in 2-dimensional images and sequential images of videos that have many methods such as local, global, and hybrid approaches.  Hidden Model Markov (HMM) is another promising method that works well for images with different lighting variations, facial expressions, and orientations. HMM is a set of statistical models used to characterize signal properties. An artificial neural network-based approach is learned from image examples and relies on techniques from machine learning to find relevant facial image characteristics. The characteristics studied were in the form of discriminant functions (ie non-linear decision surfaces), then used for face recognition. In this study there will be an application to compare Hidden Markov Models and Neural Network Method as a Face Recognition Technology Algorithm Model.  

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