scholarly journals ANÁLISE DE MÉTODOS DE DETECÇÃO E RECONHECIMENTO DE FACES UTILIZANDO VISÃO COMPUTACIONAL E ALGORITMOS DE APRENDIZADO DE MÁQUINA

2021 ◽  
Vol 13 (2) ◽  
pp. 01-11
Author(s):  
Lucas José da Costa ◽  
Thiago Luz de Sousa ◽  
Francisco Assis da Silva ◽  
Leandro Luiz de Almeida ◽  
Danillo Roberto Pereira ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Comparative Analysis ◽  
Execution Time ◽  
Digital Images ◽  
Facial Recognition ◽  
Detection Algorithm ◽  
Ambient Lighting ◽  
The Face ◽  
Haar Cascade

The advancement in technology in recent decades has provided many facilities for humanity in various applications, and facial recognition technology is one of them. There are several problemsto be solved to perform face recognition from digital images, such as varying ambient lighting, changing the face physical characteristics and resolution of the images used. This work aimed to perform a comparative analysis between some of thedetection and facial recognition methods, as well as their execution time. We use the Eigenface, Fisherface and LBPH facial recognition algorithms in conjunction with the Haar Cascade facedetection algorithm, all from the OpenCV library. We also explored the use of CNN neural network for facial recognition in conjunction with the HOG facial detection algorithm, these from the Dlib library. The work aimed, besides analyzing the algorithms in relation to hit rates, factors such as reliability and execution time were also considered

scholarly journals Comparative analysis of selected facial recognition algorithms

2020 ◽  
Vol 39 (3) ◽  
pp. 896-904
Author(s):  
J.A. Popoola ◽  
C.O. Yinka-Banjo
Keyword(s):  
Face Recognition ◽  
Comparative Analysis ◽  
Facial Recognition ◽  
Research Work ◽  
Cost Effective ◽  
Facial Symmetry ◽  
Face Matching ◽  
Computer Based ◽  
Haar Cascade ◽  
Detection And Recognition

Systems and applications embedded with facial detection and recognition capabilities are founded on the notion that there are differences in face structures among individuals, and as such, we can perform face-matching using the facial symmetry. A widely used application of facial detection and recognition is in security. It is important that the images be processed correctly for computer-based facial recognition, hence, the usage of efficient, cost-effective algorithms and a robust database. This research work puts these measures into consideration and attempts to determine a cost-effective and reliable algorithm out of three algorithms examined. Keywords: Haar-Cascade, PCA, Eigenfaces, Fisherfaces, LBPH, Face Recognition.

scholarly journals Age-based facial recognition using convoluted neural network deep learning algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 424
Author(s):  
Julius Yong Wu Jien ◽  
Aslina Baharum ◽  
Shaliza Hayati A. Wahab ◽  
Nordin Saad ◽  
Muhammad Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Learning Algorithm ◽  
Facial Recognition ◽  
Well Being ◽  
Deep Learning Algorithm ◽  
Business Applications ◽  
Image Dataset ◽  
The Face ◽  
The Individual

Face recognition is the use of biometric innovations that can see or validate a person by seeing and investigating designs depending on the shape of the individual. Face recognition is used largely for the purpose of well-being, despite the fact that passion for different areas of use is growing. Overall, face recognition innovations are worth considering because they have the potential for broad legal jurisdiction and different business applications. It is widely used in many spaces. How it works is a product of facial recognition processing facial geometry. The hole between the ear and the good way from the front to the jaw are the main variables. This code distinguishes the highlight of the face that is important for your facial separation and creates your facial expression. Therefore, this study gives an overview of age detection using a different combination of machine learning and image processing methods on the image dataset.

scholarly journals Feature Based Face Recognition using Machine Learning Technniques

2020 ◽  
Vol 8 (6) ◽  
pp. 1313-1317
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Face Recognition ◽  
Euclidean Distance ◽  
Human Face ◽  
Cosine Transformation ◽  
The Face ◽  
Feature Based ◽  
Haar Cascade ◽  
Distinct Features

Human Face has Numerous unique Features to Distinguish between each other. Face can Identified by distinguishing between face and non-face followed by Identification. Traditionally face recognition uses distinct features Comparison to Identify the Faces which is Complex for larger databases and ambiguous in many scenarios. To improve the accuracy and Scalability Proposed method uses machine learning based Haar Cascade technique for face detection and convolutional neural network is used for feature extraction followed by classification using Euclidean distance and cosine transformation to recognize the face. The results demonstrate the work is performed well in recognizing the face efficiently with different variations.

scholarly journals Intelligent and Secured Bag using Artificial Intelligence

2020 ◽  
Vol 9 (1) ◽  
pp. 2237-2240
Keyword(s):  
Artificial Intelligence ◽  
Face Recognition ◽  
Detection Algorithm ◽  
Raspberry Pi ◽  
Aged People ◽  
The Face ◽  
Haar Cascade ◽  
Almost All ◽  
Application Specific ◽  
Security Check

The Intelligent and Secured Bag is an application-specific design that can be useful for the security of important documents and valuable materials. The bag can carry out various features for daily use such as security check using face recognition. The system uses Artificial Intelligence for more effective results in terms of security in comparison with the existing system which uses fingerprint scanner. The Secured Bag consists of the facility of face recognition for advance security solution. The face recognition with Haar Cascade Classifier which is a machine learning object detection algorithm is used for the locking and unlocking of the bag which contributes in the intelligent part of the project. In order to reduce the forgetfulness of senior citizens and even professionals to pack the required items, RF-ID Technology will be used. It maintains the list of objects present in the bag. The RF-ID tags are attached to all the objects which is to be placed inside the bag. The RF-ID reader is used to read the tags which enters the bag. When any object will be missing from the bag, the message of the list of objects missing is send to the users mobile. For the security of the bag from thefts, magnetic lock is introduced. When the face of the person accessing the bag is not matched with the already existing database indicating that an unauthorized person is trying to open the bag, the lock will remain in the locked position. Thus, the person cannot access the bag. When the face of the person accessing the bag matches with the already existing database indicating that an authorized person is trying to open the bag, the lock will be unlocked and the person can access the bag. All the alert messages and the message of the list of items present and missing from the bag is sent to the owner using a GSM modem. The main advantage of using the Smart Bag is protection from thefts, also the owner of the bag gets informed about the theft and the items missing from the bag through GSM. Raspberry Pi will control all the distinguishable features. The smart bag can be used by almost all people including students, doctors, military people, aged people, etc. In general, it can be used in the daily life without the fear of something being stolen or missing from the bag.

scholarly journals Implementasi Convolutional Neural Network untuk Facial Recognition

2021 ◽  
Vol 20 (2) ◽  
pp. 66-79
Author(s):  
Dhanny Setiawan ◽  
Andikha Dwi Putra ◽  
Kezia Stefani ◽  
Jenisa Felisa
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Facial Recognition ◽  
Haar Cascade

Facial recognition merupakan salah satu teknik biometrik. Teknik yang dapat disebut juga pengenalan wajah ini telah menjadi topik yang cukup diminati untuk diteliti. Pada peneitian ini dilakukan proses pengenalan wajah dengan menggunakan metode CNN (Convolutional Neural Network). Penelitian ini memiliki tujuan untuk mengimplementasikan metode CNN ke dalam pengenalan wajah dengan menggunakan library Tensorflow. Metode ini digunakan karena proses pembelajaran dilakukan dengan mendalam (deep learning). Metode CNN yang digunakan memiliki beberapa lapisan pada proses training yang dilakukan, yaitu lapisan Conv2D, MaxPooling2d, Flatten, dan Dense. Face recognition yang dihasilkan terdapat pendeteksi wajah menggunakan Haar Cascade dengan bantuan library Opencv di dalamnya. Jumlah dataset juga diketahui dapat mempengaruhi hasil pengenalan dan proses pengenalan wajah dengan CNN juga memerlukan dataset yang besar. Adapun jumlah citra wajah yang digunakan dalam penelitian ini sebanyak 90.000 gambar wajah yang berasal dari 36 himpunan gambar dan menghasilkan tingkat akurasi sebesar 97%.

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 Face Recognition based Smart Lab for Energy Conservation

Webology ◽  
2021 ◽  
Vol 18 (SI02) ◽  
pp. 32-41
Author(s):  
M. Karthikeyan ◽  
T.S. Subashini ◽  
M.S. Prashanth
Keyword(s):  
Face Recognition ◽  
Object Identification ◽  
Home Automation ◽  
Human Beings ◽  
Environmentally Conscious ◽  
Repetitive Tasks ◽  
Automation Systems ◽  
The Face ◽  
Identification Technique ◽  
Haar Cascade

Home automation offers a good solution to help conserve our natural resources in a time when we are all becoming more environmentally conscious. Home automation systems can reduce power consumption and when they are not in use automatically turn off lights and appliances. With home automation, many repetitive tasks can be performed automatically or with fewer steps. For example, each time the person gets out of his computer desk, for instance, the fan and the lights need to be turned off and switched on when he comes back to the computer desk. This is a repetitive task, and failure to do so leads to a waste of energy. This paper proposes a security/energy saving system based on face recognition to monitor the fan and lights depending on the presence or absence of the authenticated user. Initially, the authenticated faces/users LBPH (Local Binary Pattern Histogram) features were extracted and modelled using SVM to construct the face profile of all authenticated users. The webcam catches the user's picture before the PC and the Haar-cascade classifier, a profound learning object identification technique is used to identify face objects from the background. The facial recognition techniques were implemented with python and linked to the cloud environment of Ada-Fruit in order to enable or disable the light and fan on the desk. The relay status is transmitted from Ada Fruit Cloud to Arduino Esp8266 using the MQTT Protocol. If the unidentified user in the webcam is detected by this device, the information in the cloud will be set to ' off ' status, allowing light and fan to be switched off. Although Passive Infrared Sensor (PIR) is widely used in home automation systems, PIR sensors detect heat traces in a room, so they are not very sensitive when the room itself is hot. Therefore, in some countries such as INDIA, PIR sensors are unable to detect human beings in the summer. This system is an alternative to commonly used PIR sensors in the home automation process.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals Face Recognition Model Using Back Propagation

2018 ◽  
Vol 7 (3.34) ◽  
pp. 237
Author(s):  
R Aswini Priyanka ◽  
C Ashwitha ◽  
R Arun Chakravarthi ◽  
R Prakash
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Face Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Face Identification ◽  
Identification System ◽  
Scientific World ◽  
The Face ◽  
Deep Learning Neural Network

In scientific world, Face recognition becomes an important research topic. The face identification system is an application capable of verifying a human face from a live videos or digital images. One of the best methods is to compare the particular facial attributes of a person with the images and its database. It is widely used in biometrics and security systems. Back in old days, face identification was a challenging concept. Because of the variations in viewpoint and facial expression, the deep learning neural network came into the technology stack it’s been very easy to detect and recognize the faces. The efficiency has increased dramatically. In this paper, ORL database is about the ten images of forty people helps to evaluate our methodology. We use the concept of Back Propagation Neural Network (BPNN) in deep learning model is to recognize the faces and increase the efficiency of the model compared to previously existing face recognition models.   

scholarly journals A Web-Based Mobile Attendance System with Facial Recognition Feature

2020 ◽  
Vol 14 (05) ◽  
pp. 193
Author(s):  
Noradila Nordin ◽  
Nurul Husna Mohd Fauzi
Keyword(s):  
Face Recognition ◽  
Mobile Devices ◽  
Conventional Method ◽  
Facial Recognition ◽  
Recognition System ◽  
Web Based ◽  
Face Recognition System ◽  
The Face ◽  
System Prototype

Attendance marking in a classroom is one of the methods used to track the student’s presence in the lecture. The conventional method that is being enforced has shown to be vulnerable, inaccurate and time-consuming especially in a large classroom. It is difficult to identify absentees and proxy attendees based on the conventional attendance marking method. In order to overcome the challenges faced in the conventional method, a web-based mobile attendance system with facial recognition feature is proposed. It incorporated the existing mobile devices with a camera and the face recognition system to allow the attendance system to be used in classrooms automatically and efficiently with minor implementation requirements. The system prototype received positive responses from the volunteers who tested the system to replace the conventional attendance marking.

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