Face detection ppt

This document summarizes a research paper that proposes a method for detecting and recognizing faces using the Viola Jones algorithm and Back Propagation Neural Network (BPNN). The paper first discusses face detection and recognition challenges. It then provides background on Viola Jones algorithm and BPNN. The proposed methodology uses Viola Jones for face detection, converts the image to grayscale and binary, then trains segments or the whole image with BPNN. Results are analyzed using training, testing and validation curves in the MATLAB neural network tool to minimize error. In under 3 sentences, this document outlines the key techniques, proposed method, and analysis approach discussed in the source research paper.

Ijetcas14 435

Iasir Journals

This document summarizes a research paper on face recognition techniques. It discusses the history of face recognition research from the 1950s to present. Early work focused on identifying faces using geometric measurements of facial features. In the 1980s, neural networks and eigenfaces approaches using principal component analysis were introduced. The document also outlines common problems in face recognition systems, including variations in scale, illumination, expression, and noise. Finally, it reviews literature on human and machine face recognition systems.

Top Cited Article in Informatics Engineering Research: October 2020

ieijjournal

Informatics is rapidly developing field. The study of informatics involves human-computer interaction and how an interface can be built to maximize user-efficiency. Due to the growth in IT, individuals and organizations increasingly process information digitally. This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. The Informatics Engineering, an International Journal (IEIJ) is a open access peer-reviewed journal that publishes articles which contribute new results in all areas of Informatics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and human-computer interaction design and establishing new collaborations in these areas.

Dissertation final report

This document is a dissertation submitted by Smriti Tikoo for the fulfillment of requirements for a Master's degree in Electronics and Communication Engineering. The dissertation focuses on facial detection using the Viola-Jones algorithm and facial recognition using a Backpropagation Neural Network. The document begins with an introduction that discusses the history and importance of facial recognition. It then covers topics like facial detection techniques, neural networks, and the proposed methodology which involves Viola-Jones for detection and a Backpropagation Neural Network for recognition. The document is organized into chapters that discuss the literature review, proposed methodology, software implementation, results and discussion, and conclusions.

Face and liveness detection with criminal identification using machine learni...

IAESIJAI

In the past, real-world photos have been used to train classifiers for face liveness identification since the related face presentation attacks (PA) and real-world images have a high degree of overlap. The use of deep convolutional neural networks (CNN) and real-world face photos together to identify the liveness of a face, however, has received very little study. A face recognition system should be able to identify real faces as well as efforts at faking utilizing printed or digital presentations. A true spoofing avoidance method involves observing facial liveness, such as eye blinking and lip movement. However, this strategy is rendered useless when defending against replay assaults that use video. The anti-spoofing technique consists of two modules: the ConvNet classifier module and the blinking eye module, which measure lip and eye movement. The results of the testing demonstrate that the developed module is capable of identifying various face spoof assaults, including those made with the use of posters, masks, or smartphones. To assess the convolutional features in this study adaptively fused from deep CNN produced face pictures and convolutional layers learned from real-world identification. Extensive tests using intra-database and cross-database scenarios on cutting-edge face anti-spoofing databases including CASIA, OULU, NUAA and replay-attack dataset demonstrate that the proposed solution methods for face liveness detection. The algorithm has a 94.30% accuracy rate.

184

vivatechijri

Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.

Resume-MdKhaled_14_12_2015

Md Khaled Abu Mahmoud

Md. Khaled Abu Mahmoud is an electrical and computer engineer seeking a position applying his skills in areas like electrical engineering, electronics, biomedical engineering, and image processing. He has a PhD in electrical and computer engineering from the University of Technology Sydney, where his thesis focused on developing methods to improve skin cancer detection. He has over 30 years of experience in engineering and management roles and research interests in areas like image processing, neural networks, and machine learning.

Face recognization using artificial nerual network

Dharmesh Tank

This document presents an overview of face recognition using artificial neural networks. It discusses the basic concepts of face recognition, issues with existing systems, and proposes a new system using discrete cosine transform (DCT) for feature extraction and an artificial neural network with backpropagation for classification. DCT is used to extract illumination invariant features and reduce dimensionality. The neural network is trained on these features to recognize faces. Thresholding rules are also introduced to improve recognition performance. Real-time applications of face recognition like Microsoft's Project Natal are mentioned.

Face and facial expressions recognition for blind people

This document discusses a system for facial recognition and expression recognition to help blind people identify faces and facial expressions. The system uses a Raspberry Pi 3 with a camera to capture faces. It then uses the Viola-Jones algorithm for face detection and PCA for facial recognition, comparing faces to a database of stored images. When a matched face is found, the system speaks the name of the person to the blind user. The goal is to enhance social interactions for the visually impaired by enabling them to recognize faces and expressions.

REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMS

Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.

Review of face detection systems based artificial neural networks algorithms

This document provides a review of face detection systems that are based on artificial neural network algorithms. It summarizes several studies that have used different types of neural networks for face detection, including: 1) Retinal connected neural networks and rotation invariant neural networks. 2) Principal component analysis combined with neural networks. 3) Convolutional neural networks, multilayer perceptrons, backpropagation neural networks, and polynomial neural networks. 4) Fast neural networks, evolutionary optimization of neural networks, and Gabor wavelet features with neural networks. Strengths and limitations of these different approaches are discussed.

Face recognition Face Identification

Kalyan Acharjya

This document provides an overview of Kalyan Acharjya's proposed work on face recognition for his M.Tech dissertation. It discusses conducting literature research on existing face recognition techniques, identifying challenges in real-time applications, and exploring standard face image databases. The presentation covers topics such as how face recognition works, applications, and concludes with plans to modify existing algorithms and compare results to related work to enhance recognition rates.

Facial Emotion Recognition: A Survey

This document provides an overview of facial emotion recognition techniques. It discusses face detection methods like Viola-Jones, multi-task cascaded convolutional neural networks (CNN), and R-CNN. For face recognition, it covers linear approaches like Eigenfaces, Fisherfaces, and Marginfaces. It also discusses CNN and HOG methods for emotion detection, like AlexNet CNN and HOG-ESRs. Popular datasets for evaluation include CK+, JAFFE, and FER2013. The document surveys key algorithms and approaches for face detection, recognition, and emotion detection.

Extracting individual information using facial recognition in a smart mirror....

IQRARANI11

PPT 7.4.2015

Sneha Muralidharan

This document presents a proposed approach for biometric authentication using facial vein patterns captured in thermal images. The system aims to address limitations of existing identification systems. It involves extracting thermal signatures from registered infrared face images using morphological operators and directional/diffusional filters. Signatures are matched against templates for authentication. The proposed approach is intended to provide a more secure and accurate identification system compared to alternatives that are sensitive to factors like lighting. It involves modules for image registration, face segmentation, signature extraction, and feature matching between extracted signatures and stored templates. Results show an average accuracy of 87.16-94.63% for the matching algorithm.

Real Time Implementation Of Face Recognition System

IJERA Editor

This paper proposes face recognition method using PCA for real time implementation. Nowadays security is gaining importance as it is becoming necessary for people to keep passwords in their mind and carry cards. Such implementations however, are becoming less secure and practical, also is becoming more problematic thus leading to an increasing interest in techniques related to biometrics systems. Face recognition system is amongst important subjects in biometrics systems. This system is very useful for security in particular and has been widely used and developed in many countries. This study aims to achieve face recognition successfully by detecting human face in real time, based on Principal Component Analysis (PCA) algorithm.

IRJET- Development of a Face Recognition System with Deep Learning and Py...

This document presents the development of a face recognition system using deep learning algorithms and PyTorch. The system was trained on a dataset of 94 face images and was able to extract relevant facial features to encode faces and recognize them with 95% accuracy. The system represents faces as 128-dimensional embeddings that allow for efficient comparison of faces within the dataset. It can recognize faces in the dataset and continuously learn by incorporating new face images to improve its predictions.

Intelligent System For Face Mask Detection

This document presents research on developing an intelligent system to detect whether people are wearing face masks or not using deep learning techniques. The system uses a convolutional neural network called MobileNetV2 trained on a dataset of 480 masked and unmasked face images. Data augmentation is used to increase the size of the dataset. OpenCV is used for face detection. The system achieves 99% accuracy on the test dataset and can classify images and video frames in real-time. Applications discussed include use in airports, hospitals, offices and by law enforcement to monitor compliance with mask mandates and prevent the spread of COVID-19.

Innovative Analytic and Holistic Combined Face Recognition and Verification M...

ijbuiiir1

Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.

Electric vehicle and photovoltaic advanced roles in enhancing the financial p...

IJECEIAES

Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network

Introduction to AI Safety (public presentation).pptx

MiscAnnoy1

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Smriti's research paper

Ijetcas14 435

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Top Cited Article in Informatics Engineering Research: October 2020

ieijjournal

Dissertation final report

Face and liveness detection with criminal identification using machine learni...

IAESIJAI

184

vivatechijri

Resume-MdKhaled_14_12_2015

Md Khaled Abu Mahmoud

Face recognization using artificial nerual network

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Face and facial expressions recognition for blind people

REVIEW OF FACE DETECTION SYSTEMS BASED ARTIFICIAL NEURAL NETWORKS ALGORITHMS

Review of face detection systems based artificial neural networks algorithms

Face recognition Face Identification

Kalyan Acharjya

Facial Emotion Recognition: A Survey

Extracting individual information using facial recognition in a smart mirror....

IQRARANI11

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Sneha Muralidharan

Real Time Implementation Of Face Recognition System

IJERA Editor

IRJET- Development of a Face Recognition System with Deep Learning and Py...

Intelligent System For Face Mask Detection