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.

scholarly journals Face Recognition Door Lock System using Raspberry Pi

2021 ◽  
Vol 9 (VI) ◽  
pp. 138-145
Author(s):  
K. V. Usha Ramani
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Initial Step ◽  
Power Input ◽  
Human Interaction ◽  
Raspberry Pi ◽  
Identification System ◽  
Human Beings ◽  
The Face ◽  
Door Lock

One of the crucial difficulties we aim to find in computer vision is to recognize items automatically without human interaction in a picture. Face detection may be seen as an issue when the face of human beings is detected in a picture. The initial step towards many face-related technologies, including face recognition or verification, is generally facial detection. Face detection however may be quite beneficial. A biometric identification system besides fingerprint and iris would likely be the most effective use of face recognition. The door lock system in this project consists of Raspberry Pi, camera module, relay module, power input and output, connected to a solenoid lock. It employs the two different facial recognition algorithms to detect the faces and train the model for recognition purpose

scholarly journals Automatic Attendance System Using Face Recognition Technique

2020 ◽  
Vol 9 (1) ◽  
pp. 2134-2138
Keyword(s):  
Face Recognition ◽  
User Interface ◽  
Graphical User Interface ◽  
Cascade Algorithm ◽  
The Face ◽  
Haar Cascade ◽  
Recognition Technique

Attendance system is very important in schools and colleges’ Manual attendance system has many difficulties like it may less accurate and critical to maintain. So, attendance system using face recognition technique increase the accuracy and also it required less time than other methods. There are many existing system for attendance such as face recognition using IoT, PIR sensors and so on. For face recognition, hardware devices also helpful. But challenge is that to maintain all the sensors properly without get damage. After studying all method and techniques we are trying to implement a system with Haar Cascade Algorithm which has highest accuracy among all. It is able to capture the images from 50-70cm. We are creating graphical user interface which capture the images, create the dataset and train the dataset on single click. After recognizing the face it will display name of student and roll number. That information stored in attendance sheet automatically with time and date.

Face Recognition and Face Detection Using Open Computer Vision Classifiers and Python

2021 ◽  
pp. 185-207
Author(s):  
Priyank Jain ◽  
Meenu Chawla ◽  
Sanskar Sahu
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Face Detection ◽  
Local Binary Pattern ◽  
Research Work ◽  
Modern World ◽  
The Face ◽  
Haar Cascade

Identification of a person by looking at the image is really a topic of interest in this modern world. There are many different ways by which this can be achieved. This research work describes various technologies available in the open-computer-vision (OpenCV) library and methodology to implement them using Python. To detect the face Haar Cascade are used, and for the recognition of face eigenfaces, fisherfaces, and local binary pattern, histograms has been used. Also, the results shown are followed by a discussion of encountered challenges and also the solution of the challenges.

scholarly journals Development of a secured room access system based on face recognition using Raspberry Pi and Android based smartphone

2018 ◽  
Vol 197 ◽  
pp. 11008 ◽  
Author(s):  
Asep Najmurrokhman ◽  
Kusnandar Kusnandar ◽  
Arief Budiman Krama ◽  
Esmeralda Contessa Djamal ◽  
Robbi Rahim
Keyword(s):  
Face Recognition ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Security Issues ◽  
Access System ◽  
The Face ◽  
Access Control System ◽  
Haar Cascade ◽  
Door Lock ◽  
Access Rights

Security issues are an important part of everyday life. A vital link in security chain is the identification of users who will enter the room. This paper describes the prototype of a secured room access control system based on face recognition. The system comprises a webcam to detect faces and a solenoid door lock for accessing the room. Every user detected by the webcam will be checked for compatibility with the database in the system. If the user has access rights then the solenoid door lock will open and the user can enter the room. Otherwise, the data will be sent to the master user via Android-based smartphone that installed certain applications. If the user is recognized by the master user, then the solenoid door lock will be opened through the signal sent from the smartphone. However, if the user is not recognized, then the buzzer will alert. The main control circuit on this system is Raspberry pi. The software used is OpenCV Library which is useful to display and process the image produced by webcam. In this paper, we employ Haar Cascade Classifier in an image processing of user face to render the face detection with high accuracy.

scholarly journals IoT Based Smart Home Security System with Face Recognition and Weapon Detection Using Computer Vision

2020 ◽  
Vol 10 (1) ◽  
pp. 336-344
Keyword(s):  
Computer Vision ◽  
Face Recognition ◽  
Smart Home ◽  
Vibration Sensor ◽  
Home Automation ◽  
Live Feed ◽  
Automation Systems ◽  
Entry Points ◽  
Sensor Signals ◽  
Valid Proof

Today home automation systems are popular in households. The control of electric fixtures like fans and lights is possible with the help of Internet of Things (IOT). The problem arises due to intrusion of burglars. The security of such systems has been done using computer vision and IOT. Here we aim to enhance this system by use of image processing for object detection. The system uses cameras at the door for face recognition as access control. Also, vibration and door magnet sensors are installed at the entry points to detect when the burglar tries to barge inside. PIR sensors are employed to detect human presence. A vibration sensor is also used to give alert if any shock nearby is detected. The system allows entry only if authorized person like owner or person registered on the database arrives. The person may be identified through valid proof of identity. It sends a message to the owner in case it doesn’t recognize the person within 20 seconds and the owner can monitor the activities via live feed from the camera. All sensor signals are checked and status of the system is updated continuously. In case the burglar tries to break inside, siren is activated and alert messages are redirected to the owner and the police.

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 STUDENT ATTENDANCE SYSTEM BASED ON THE FACE RECOGNITION

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Harshit Agarwal ◽  
Govinda Verma ◽  
Lakshya Gupta
Keyword(s):  
Face Recognition ◽  
Recognition System ◽  
Graphical Interface ◽  
Student Attendance ◽  
Cascade Algorithm ◽  
Face Recognition System ◽  
The Face ◽  
Haar Cascade

Attendance system is very important in schools and colleges' The student attendance program has many problems such as it may not be accurate and critical to maintain. Therefore, an existing system that uses a face recognition system increases accuracy and also requires less time than other methods. There are many systems available such as face recognition using IoT, PIR sensors and so on. With face recognition, hardware devices are helpful. But the challenge is to keep all the nerves properly without getting hurt. After learning all the techniques and techniques we try to use the system with Haar Cascade Algorithm with the highest accuracy among them all. It can take pictures from 50- 70cm. We create a graphical interface that takes pictures, builds a database and trains the database with a single click. After seeing the face it will show the student's name and roll number. That information is stored on an automatic attendance sheet by time and date.

scholarly journals Real Time Face Recognition in Group Images using LBPH

2019 ◽  
Vol 8 (2) ◽  
pp. 1362-1367
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Real Time ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Testing Dataset ◽  
Negative Images ◽  
The Face ◽  
Haar Cascade

Face recognition is a beneficial work in computer vision based applications. The goal of the proposed system is to provide complete face recognitions system capable of working a group of images. The faces are detected and verified the identity of an individual using a machine learning algorithm. The haar cascade detects the face from a group of images for training and testing dataset. The dataset contained positive and negative images for training and testing. The LBPH algorithm recognizes the faces from input images. The proposed system detects and recognizes faces with 98% accuracy

scholarly journals Multi-Object Face Recognition Using Local Binary Pattern Histogram and Haar Cascade Classifier on Low-Resolution Images

2021 ◽  
Vol 11 (1) ◽  
pp. 45-58
Author(s):  
R. Rizal Isnanto ◽  
Adian Rochim ◽  
Dania Eridani ◽  
Guntur Cahyono
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
Local Binary Pattern ◽  
Video Data ◽  
Cascade Classifier ◽  
Low Resolution ◽  
The Face ◽  
Haar Cascade ◽  
Pattern Histogram ◽  
Low Resolution Images

This study aims to build a face recognition prototype that can recognize multiple face objects within one frame. The proposed method uses a local binary pattern histogram and Haar cascade classifier on low-resolution images. The lowest data resolution used in this study was 76 × 76 pixels and the highest was 156 × 156 pixels. The face images were preprocessed using the histogram equalization and median filtering. The face recognition prototype proposed successfully recognized four face objects in one frame. The results obtained were comparable for local and real-time stream video data for testing. The RR obtained with the local data test was 99.67%, which indicates better performance in recognizing 75 frames for each object, compared to the 92.67% RR for the real-time data stream. In comparison to the results obtained in previous works, it can be concluded that the proposed method yields the highest RR of 99.67%.

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

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