COMPARISON OF FACE RECOGNITION ALGORITHMS FOR SURVEILLANCE OF CHILDREN USING OPENCV.

2021 ◽  
Vol 1 (10) ◽  
pp. 328-332
Author(s):  
Sourabh Kumar ◽  
Bhaskar Kapoor Kapoor
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Security System ◽  
Human Face ◽  
Security Measures ◽  
Recognition Algorithms ◽  
The Face ◽  
Haar Cascade

Proposing a security system for surveillance of home alone children for safety purpose and send an alert to the register mobile number if some kind of intrusion is detected. I have used Viola-Jones algorithm to detect human face from the live camera and then frame is resized then resized image is processed by the Local Binary Pattern Histograms (LBPH) algorithm and save the model in a YML file and then it is implemented on live cam feed in which the algorithm will detect the face and if some unknown face has been identified it will trigger a notification to the registered mobile number using a python library named [Pywhatkit] so the user can perform security measures. Keywords: Face recognition, Open-CV, HAAR cascade, face recognition.

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 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 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 FACIAL RECOGNITON BASED DOOR LOCKING AND UNLOCKING MECHANISM BASED ON IOT

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Purobi Parasar ◽  
Hirockjyoti Deka ◽  
Bishal Saikia ◽  
Nilabh Anjan Chutia ◽  
Dr. Purnendu Bikash Acharjee
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Raspberry Pi ◽  
Computer Language ◽  
Recognition Algorithms ◽  
Locking Mechanism ◽  
The Face ◽  
Pattern Histogram

Now a days Security Has Become an Important Issue to Be Resolved. So, We Are Solving That Matter by Using Updated Technology. This Project “Face Recognition Based Door Unlocking System” deals with The Conception to Protect Locking Mechanism Utilizing IoT for Door Unlocking Process to Give Essential Security to Our House, Bank Lockers and others. In This Project We Look into the Accuracy of The Face Recognition Algorithms Using OpenCV And Python Computer Language. Local Binary Pattern Histogram Algorithm Is Used for Face Recognition. Training and Identification Is Done in Raspberry pi.

Real Time Person Recognition and Attendance Marking Using Holistic Based Approach

2017 ◽  
Vol 7 (7) ◽  
pp. 302
Author(s):  
Reshma P ◽  
Muneer VK ◽  
Muhammed Ilyas P
Keyword(s):  
Face Recognition ◽  
Real Time ◽  
High Accuracy ◽  
Efficient Technique ◽  
Human Face ◽  
Traditional Methods ◽  
Person Recognition ◽  
The Face

Face recognition is a challenging task for the researches. It is very useful for personal verification and recognition and also it is very difficult to implement due to all different situation that a human face can be found. This system makes use of the face recognition approach for the computerized attendance marking of students or employees in the room environment without lectures intervention or the employee. This system is very efficient and requires very less maintenance compared to the traditional methods. Among existing methods PCA is the most efficient technique. In this project Holistic based approach is adapted. The system is implemented using MATLAB and provides high accuracy.

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.

Adaptive GLOH with PSO-trained NN for the recognition of plastic surgery faces and their types

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Archana Harsing Sable ◽  
Sanjay N. Talbar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Plastic Surgery ◽  
Local Binary Pattern ◽  
Principal Component ◽  
Traditional Learning ◽  
Swarm Optimization ◽  
The Neural Network ◽  
Orientation Histogram ◽  
The Face

Abstract Numerous algorithms have met complexity in recognizing the face, which is invariant to plastic surgery, owing to the texture variations in the skin. Though plastic surgery serves to be a challenging issue in the domain of face recognition, the concerned theme has to be restudied for its hypothetical and experimental perspectives. In this paper, Adaptive Gradient Location and Orientation Histogram (AGLOH)-based feature extraction is proposed to accomplish effective plastic surgery face recognition. The proposed features are extracted from the granular space of the faces. Additionally, the variants of the local binary pattern are also extracted to accompany the AGLOH features. Subsequently, the feature dimensionality is reduced using principal component analysis (PCA) to train the artificial neural network. The paper trains the neural network using particle swarm optimization, despite utilizing the traditional learning algorithms. The experimentation involved 452 plastic surgery faces from blepharoplasty, brow lift, liposhaving, malar augmentation, mentoplasty, otoplasty, rhinoplasty, rhytidectomy and skin peeling. Finally, the proposed AGLOH proves its performance dominance.

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