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Improving the Efficiency of Image Captured via PGC and Stereo Vision Using Novel VJ Algorithm in Comparison with local binary pattern histogram Algorithm 2

Improving the Efficiency of Image Captured via PGC and Stere...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Ramisetty, Harshitha Bhavani, N.P.G. Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350306415

The study compares the local binary pattern histogram method to a PGC and stereo vision picture acquired utilising innovative violas jones algorithm. An automated face identification and recognition system was tested utilising the innovative violas jones algorithm and local binary pattern histogram (LBPH) Algorithm using 20 samples at various periods. The G power with 0.8 pre-testing power, 0.05 alpha, and 0.95 confidence interval is suggested for SPSS accuracy prediction. The model is tested and trained on 40 kaggle (***) samples. Automatic face identification and recognition system performance was assessed using the innovative violas jones method and local binary pattern histogram method with 20 samples at various periods. After statistical analysis, the violas jones algorithm achieved 93% accuracy, while the local binary pattern histogram algorithm achieved 84% accuracy using the default setting. The independent sample t-test significance was =0.001 ( © 2024 IEEE.

关键词： Face detection local binary pattern histogram Novel Violas Jones Algorithm Technology Transfer

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An Improved Real-Time Face Recognition System at Low Resolution Based on local binary pattern histogram Algorithm and CLAHE

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Optics and Photonics Journal 2021年第4期11卷 63-78页

作者： Kamal Chandra Paul Semih Aslan Electrical and Computer Engineering The University of North Carolina at Charlotte North Carolina USA Ingram School Engineering Texas State University San Marcos TX USA

This research presents an improved real-time face recognition system at a low resolution of 15 pixels with pose and emotion and resolution variations. We have designed our datasets named LRD200 and LRD100, which have been used for training and classification. The face detection part uses the Viola-Jones algorithm, and the face recognition part receives the face image from the face detection part to process it using the Local Binary Pattern Histogram (LBPH) algorithm with preprocessing using contrast limited adaptive histogram equalization (CLAHE) and face alignment. The face database in this system can be updated via our custom-built standalone android app and automatic restarting of the training and recognition process with an updated database. Using our proposed algorithm, a real-time face recognition accuracy of 78.40% at 15 px and 98.05% at 45 px have been achieved using the LRD200 database containing 200 images per person. With 100 images per person in the database (LRD100) the achieved accuracies are 60.60% at 15 px and 95% at 45

关键词： Face Detection Face Recognition Low Resolution Feature Extraction Security System Access Control System Viola-Jones Algorithm LBPH local binary pattern histogram

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Implementation of RFID Attendance System with Face Detection using Validation Viola-Jones and local binary pattern histogram Method 4

Implementation of RFID Attendance System with Face Detection...

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4th International Symposium on Electronics and Smart Devices (ISESD) - Future Smart Devices and Nanotechnology for Microelectronics

作者： Basthomi, Faisal Rohman Nasikhin, Khoirun Sa'adah, Roudhotul Auliya Prasetyo, Dendy Dwi Syai'in, Mat Rinanto, Noorman Endrasmono, Joko Indarti, Rini Setiyoko, Annas Singgih Sukoco, Didik Herijono, Budi Soeprijanto, Adi Adi Shipbldg Inst Polytech Surabaya Automat Engn Study Program Surabaya 60111 Indonesia Shipbldg Inst Polytech Surabaya Marine Elect Engn Study Program Surabaya 60111 Indonesia Shipbldg Inst Polytech Surabaya Marine Elect Engn Study Program Surabaya 60111 Indonesia Sepuluh Nopember Inst Techhnol Surabaya 60111 Indonesia

ISBN: (纸本)9781728121567

In this study, attendance system was created by combines RFID attendance technology and face recognition. The attendance system is an important thing to facilitate attendance data retrieval. Attendance system often encountered in the technological era such as today is the RFID attendance system. Although the attendance system is already sophisticated, this system still has shortcomings such as the occurrence of entrusted cards. In this research, the writer has two inputs, namely ID on the RFID card and a face image. Face images will be process by two methods there are the Viola-Jones method that use to detecting the face objects in the image, and the local binary pattern histogram method as face recognition. Furthermore, if the face was recognized then the RFID card ID data and face recognition data will compare, the results of this process will be attended data in the database. Based on the results of test have been done by the author, the response system shows very good results. In the first test of the RFID sensor in 11 tests, the sensor was able to distinguish all cards that had been registered and cards that had not been registered. This test is carried out from distance of 1 cm to 4 cm. Furthermore, in the second test of facial recognition, in 100 trials of facial recognition testing with different facial poses, the system can recognize faces 93 times so system has 93% success rate. This success rate is influenced by several factors such as the test carried out at 364 lux illumination strength, the test distance at > 20cm and < 70cm, as well as the maximum testing angle 45 degrees.

关键词： Attendance System RFID Facial Recognition Viola-Jones local binary pattern histogram

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Multi-scale local binary pattern histogram for Gender Classification 8

Multi-scale Local Binary Pattern Histogram for Gender Classi...

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Proceedings 2015 8 International Congress on Image and Signal Processing (CISP)

作者： Xu, Yanan Zhao, Yong Zhang, Yongjun Peking Univ Shenzhen Grad Sch Coll Informat Engn Shenzhen Peoples R China Guizhou Univ Coll Comp Sci & Technol Guiyang Peoples R China

ISBN: (纸本)9781467390989

LBP (local binary pattern) is a commonly used operator to extract LBPH (LBP histogram) of an image for local texture description. For gender classification, we proposed an innovative method by extracting multi-scale LBPH in DoG (Difference of Gaussian) space in this paper. Given a facial image, we firstly preprocess it meticulously to avert the local variations of images which probably be caused by expression, pose and so on. And then we extract multi-scale LBPH features in DoG space which can extract richer local and global interested information of the facial image. Gender classification is performed via a standard binary classifier: SVM. We conducted experiment on 2,410 FERET images and 1,100 images of our collected dataset from the Internet. The best performance of 97.7% is achieved, and the method also performs robustly.

关键词： Gender Classification local binary pattern histogram DoG (Difference of Gaussian) SVM

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Investigation of Student Engagement Monitoring System using Machine Learning 2

Investigation of Student Engagement Monitoring System using ...

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2nd International Conference on Inventive Computing and Informatics (ICICI)

作者： Abraham, Amish G. Issac, Titus Terrance, J. Sebastian Karunya Inst Technol & Sci Div Comp Sci & Engn Coimbatore Tamil Nadu India Karunya Inst Technol & Sci Div Artificial Intelligence & Machine Learning Coimbatore Tamil Nadu India

ISBN: (纸本)9798350373301;9798350373295

This research study aims to enhance the intelligent classroom ecosystem by integrating the local binary pattern histogram (LBPH) algorithm with human expertise to identify students' faces. Monitoring student attentiveness is crucial for effective learning. To support this, this study uses Mediapipe to analyze students' body posture, providing insights into their engagement levels. Additionally, the proposed framework includes the Eye Aspect Ratio (EAR) Algorithm to detect signs of drowsiness and yawning. By continually refining the proposed framework, this study intends to improve intelligent classroom ecosystems, helping educators to create environments that increase student success.

关键词： Face identification local binary pattern histogram Student Attentiveness Body Posture Eye Tracing Mediapipe Yawn Drowsiness Eye aspect ratio

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Deep Learning-Powered Face Recognition Attendance System: A Novel Approach to Automated Prediction 2

Deep Learning-Powered Face Recognition Attendance System: A ...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Sornalakshmi, M. Abinash, M.J. Gopalakrishnan, S. Kasthuri, R. Nivedhitha Harini, C.M. Balamurugan, K. Department of Computer Science PG Arulmigu Kalasalingam College of Arts and Science Tamilnadu India Arulmigu Kalasalingam College of Arts and Science Tamilnadu India

ISBN: (纸本)9798350340235

Taking attendance is one of the most critical activities that must be completed on a regular basis at colleges, universities, organizations, schools, and companies. The bulk of the time, it is done manually, such as by name or roll number. The primary purpose of this work is to develop a Face Recognition-based attendance system that will automate this human procedure. This research work fits the standards for modernizing the way attendance is handled, as well as the time management criteria. This gadget is put in the classroom and is programmed with student information such as name, roll number, class, sec, and photos. Open Computer Vision (CV) is used to extract the photos. The understudy can move toward the machine before the beginning of the connected example, and it will start taking photos and contrasting them with the passing dataset. The camera and processor board in this task were a Logitech C270 web camera and a NVIDIA Jetson Nano Engineer pack. The image is changed as follows: Initial, a Haar Cascade classifier is utilized to recognize faces, trailed by the LBPH (Neighborhood Twofold Example histogram) Calculation, histogram information is considered in contrast to a preset dataset, and the gadget consequently distinguishes participation. A Succeed document is made and refreshed consistently with data from the pertinent class educator. The accompanying work summarizes the fundamental elements of a facial recognition -based attendance system. © 2023 IEEE.

关键词： Face Recognition local binary pattern histogram Open Computer Vision

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Automatic Attendance Monitoring System (AAMS) 2nd

Automatic Attendance Monitoring System (AAMS)

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2nd Conference of Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2020

作者： Pandey, Abhinav Santosh Verma, Kumar Mayank Verma, Tanya Kumar, Abhay School of Computing Science Department Galgotias University Greater Noida India

ISBN: (纸本)9789811902956

As We Know Marking Attendance manually is something that takes more time and also leads to distraction of class whilst taking attendance. This form of taking attendance didn’t guarantee the safety of the whole data. So, we automated this system by creating an Automated Attendance Monitoring Application that will manage all the tasks related to attendance marking. This application is based on facial recognition technology which is nothing but identification of a particular person by peculiar features of their faces and it functions as a clear identifier of anyone (Matilda and Shahin in 2019 IEEE international conference on system, computation, automation and networking (ICSCAN), pp. 1–4, 2019[1]). As computer vision (CV) is growing day by day as it is technology that takes images/videos as its input and performs classification of images, detection and recognition of given input images (Deeba et al. in Int. J. Adv. Comput. Sci. Appl. 2019[2]). Its functionality is simply we had to move our camera into room and it will detect the faces and later train the images by our pre-trained model i.e. Haar Cascade Files and then recognizes every face and marks the attendance by matching with stored images data in firebase and stores attendance in csv file and this will save time and hence will avoid fake proxies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer vision (CV) Face recognition Firebase Haar cascade file local binary pattern histogram

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Real time automatic face recognition system using LBPH technique

Real time automatic face recognition system using LBPH techn...

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IEEE Chilean Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

作者： Rojas Flores, Ricardo Jamett Dominguez, Marcela Univ Santiago de Chile Dept Ingn Elect Santiago Chile Univ Santiago de Chile Ciencias Ingn Ingn Elect Santiago Chile

ISBN: (纸本)9781665408738

The aim of this paper is the development of a facial recognition system, implemented through software, using Python and OpenCV. Haarcascade and LBPH techniques are applied in the preprocessing, training, and recognition of faces from various databases and images obtained from a webcam. The main successful and unsuccessful results are presented, as well as the analysis of the training time for different amounts of images and organization in folders.

关键词： Computer vision Haarcascade local binary pattern histogram OpenCV Python

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An Efficient Convolutional Neural Network Approach for Facial Recognition 10

An Efficient Convolutional Neural Network Approach for Facia...

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10th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： Mangal, Aayushi Malik, Himanshu Aggarwal, Garima Amity Univ ASET Dept Comp Sci Noida UP India

ISBN: (纸本)9781728127910

Data security being the main concern now a days, has faced a lot of threat in terms of breaching of information which requires immediate attention. Biometrics have served a long-run for this purpose which is a part of Deep Learning. In the recent past, face recognition has become a very important tool for safety and security purposes. This paper presents the application of face recognition technique, making use of Convolutional Neural Network (CNN) with Python and a comparison is drawn between the other techniques such as Principal Component Analysis (PCA), local binary pattern (LBP) and K Nearest Neighbour (KNN). Unlike conventional methods, the proposed scheme uses four Convolutional layers with ReLu layers, four pooling layers, a fully connected layer and a Softmax Loss Layer to normalize the probability distribution. The dataset consists of 1500 images with different facial expressions and the model is trained and tested in order to acquire an accuracy using CNN method. Experimental results show that the proposed Neural Network scored an accuracy of 96.96%.

关键词： Facial Recognition Deep Learning Face Database Convolutional Neural Networks K Nearest Neighbour Principal Component Analysis local binary pattern histogram

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Performance Comparison of Detection, Recognition and Tracking Rates of the different Algorithms

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第6期10卷 153-158页

作者： Kavuri, Meghana Prakash, Kolla Bhanu Koneru Laksmaiah Educ Fdn Dept Comp Sci & Engn Guntur India

This article discusses the approach of human detection and tracking in a homogeneous domain using surveillance cameras. This is a vast area in which significant research has been taking place from more than a decade and the paper is about detection of a human and its face in a given video and stores local binary pattern histogram (LBPH) features of the detected faces. Once a human is detected in the video, that person will be given a label and him/her is tracked in different video taken by multiple cameras by the application of machine learning and image processing with the help of OpenCV. Many algorithms were used for detecting, recognizing and tracking till date, thus in this paper, main thing is the comparison of the proposed algorithm with some among the state-of-the-art algorithms. And also shows how the proposed algorithm is better than the other chosen algorithms.

关键词： Detection recognition tracking local binary pattern histogram Kalman filter particle filter

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