Download or read book Live Video Face Detection and Recognition written by Jalwinder Pingal and published by . This book was released on 2010 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Face Detection and Recognition written by Asit Kumar Datta and published by CRC Press. This book was released on 2015-10-28 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control.Face Detection and Recognition: Theory and Practice elaborates on and explains the theory and practice of face de

Download or read book Handbook of Face Recognition written by Stan Z. Li and published by Springer Science & Business Media. This book was released on 2005-12-06 with total page 394 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the history of computer-aided face recognition stretches back to the 1960s, automatic face recognition remains an unsolved problem and still offers a great challenge to computer-vision and pattern recognition researchers. This handbook is a comprehensive account of face recognition research and technology, written by a group of leading international researchers. Twelve chapters cover all the sub-areas and major components for designing operational face recognition systems. Background, modern techniques, recent results, and challenges and future directions are considered. The book is aimed at practitioners and professionals planning to work in face recognition or wanting to become familiar with the state-of- the-art technology. A comprehensive handbook, by leading research authorities, on the concepts, methods, and algorithms for automated face detection and recognition. Essential reference resource for researchers and professionals in biometric security, computer vision, and video image analysis.

Download or read book Consistent and Accurate Face Tracking and Recognition in Videos written by Yiran Liu and published by . This book was released on 2020 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatically tracking and recognizing human faces in videos and live streams is often a crucial component in many high-level applications such as security, visual surveillance and human-computer interaction. Deep learning has recently revolutionized artificial intelligence areas, including face recognition and detection. Most of the existing video analysis solutions, however, rely on certain 2D convolutional neural network (CNN) to process video clips upon a frame-to-frame basis. The temporal contextual information between consecutive frames is often inadvertently overlooked, resulting in inconsistent tracking outcomes, which also negatively affect the accuracy of human identification. To provide a remedy, we propose a novel network framework that allows history information be carried along video frames. More specifically, we take the single short scale-invariant face detection (S3FD) as the baseline face detection network and combine it with long short-term memory (LSTM) components to integrate temporal context. Taking the images and detection results of previous frames as additional inputs, our S3FD + LSTM framework is well posed to produce more consistent and smoother face detection results along time, which in return leads to more robust and accurate face recognition in videos and live streams. We evaluated our face tracking and recognition model with both public (YouTube Face) and self-made datasets. Experimental results demonstrate that our S3FD+LSTM approach constantly produces smoother and more stable bounding boxes than S3FD alone. Recognition accuracy is also improved over the baseline model, and our model significantly outperforms the state-of-the-art face tracking solutions in the public domain.

Download or read book Face Recognition for Real Time Application written by Pradeep Kakkar and published by GRIN Verlag. This book was released on 2017-11-27 with total page 103 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2017 in the subject Engineering - Computer Engineering, grade: 10, , course: M.Tech-ECE, language: English, abstract: Images containing faces are essential to intelligent vision-based human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. The rapidly expanding research in face processing is based on the premise that information about a user’s identity, state, and intent can be extracted from images and that computers can then react accordingly, e.g., by knowing person’s identity, person may be authenticated to utilize a particular service or not. A first step of any face processing system is registering the locations in images where faces are present. The local binary pattern is a simple yet very efficient texture operator which labels the pixels of an image by thresholding the neighborhood of each pixel and considers the result as a binary number. The LBP method can be seen as a unifying approach to the traditionally divergent statistical and structural models of texture analysis. Perhaps the most important property of the LBP operator in real-world applications is its invariance against monotonic gray level changes caused, e.g., by illumination variations. Another equally important is its computational simplicity, which makes it possible to analyze images in challenging real-time settings. The success of LBP in face description is due to the discriminative power and computational simplicity of the LBP operator, and the robustness of LBP to mono-tonic gray scale changes caused by, for example, illumination variations. The use of histograms as features also makes the LBP approach robust to face misalignment and pose variations. For these reasons, the LBP methodology has already attained an established position in face analysis research. Because finding an efficient spatiotemporal representation for face analysis from videos is challenging, most of the existing works limit the scope of the problem by discarding the facial dynamics and only considering the structure. Motivated by the psychophysical findings which indicate that facial movements can provide valuable information to face analysis, spatiotemporal LBP approaches for face, facial expression and gender recognition from videos were described.

Download or read book Face Recognition and Video Face Tracking written by Deepa Seetharamaiah and published by . This book was released on 2011 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project is about human face recognition in image files and human face detection in video streaming. Face recognition involves matching a given image with the database of images and identifying the image that it resembles the most. Face detection is a computer technology that determines the locations and sizes of human faces in digital images. In this project, face recognition is done using two methods: (a) Eigen faces and (b) applying Principal Component Analysis (PCA) on wavelet sub-band image. In the first face recognition method, Eigen faces technique is used. Development is done in Java. The second method for face recognition here is to apply Principal Component Analysis (PCA) on wavelet sub-band image. Instead of using the entire image for creating the test database and sample test image, the images are decomposed into frequency sub-bands. This provides greater computational speed when the test database is huge because low resolution decomposed images are used instead of high resolution images. This is also done in Java using standard Java Graphical User Interface packages for Wavelet Transform. For face detection, Haar-like feature based method is used here. Haar-like features are digital image features used in object recognition that encode information about the object to be detected. Java Media Framework (JMF) library is used to show the video captured from the web camera in a Java application. Java imaging libraries are used to develop the algorithm for the face detection. The aim of this project is to successfully demonstrate the human face recognition using Eigen faces and Wavelet sub-band methods and also to detect human face in a video streaming.

Download or read book Face Detection and Modeling for Recognition written by Rein-Lien Hsu and published by . This book was released on 2002 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face recognition has received substantial attention from researchers in biometrics, computer vision, pattern recognition, and cognitive psychology communities because of the increased attention being devoted to security, man-machine communication, content-based image retrieval, and image/video coding. We have proposed two automated recognition paradigms to advance face recognition technology. Three major tasks involved in face recognition systems are: (i) face detection, (ii) face modeling, and (iii) face matching. We have developed a face detection algorithm for color images in the presence of various lighting conditions as well as complex backgrounds. Our detection method first corrects the color bias by a lighting compensation technique that automatically estimates the parameters of reference white for color correction. We overcame the difficulty of detecting the low-luma and high-luma skin tones by applying a nonlinear transformation to the Y CbCr color space. Our method generates face candidates based on the spatial arrangement of detected skin patches. We constructed eye, mouth, and face boundary maps to verify each face candidate. Experimental results demonstrate successful detection of faces with different sizes, color, position, scale, orientation, 3D pose, and expression in several photo collections. 3D human face models augment the appearance-based face recognition approaches to assist face recognition under the illumination and head pose variations. For the two proposed recognition paradigms, we have designed two methods for modeling human faces based on (i) a generic 3D face model and an individual's facial measurements of shape and texture captured in the frontal view, and (ii) alignment of a semantic face graph, derived from a generic 3D face model, onto a frontal face image.

Download or read book Data Science and Machine Learning Series Facial Detection and Recognition Using OpenCV BONUS Create Your Own Snapchat Filter written by Advait Jayant and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply facial recognition using OpenCV in this course within the Data Science and Machine Learning Series. Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to practice facial recognition software, including one project where you will build your own Snapchat Filter! Also here are all of Advait Jayant's highly-rated videos on O'Reilly, including the full Data Science and Machine Learning Series . The following eight topics will be covered in this Data Science and Machine Learning course: Introducing Computer Vision and OpenCV . Be able to explain how computer vision works in this first topic in the Data Science and Machine Learning Series. Computer vision is the way of teaching intelligence to machines and teaching machines to view the world just as humans do. Examples are provided such as self-driving cars. Learn about OpenCV (Open Source Computer Vision Library). This library contains over 2,500 optimized computer vision and machine learning algorithms. Learn that digital images are stored in a matrix, and that grayscale images are single channel and colored images have three channels. Installing OpenCV and Working with Images . Install OpenCV and start working with images in this second topic in the Data Science and Machine Learning Series. Reading a Video Stream from the Webcam using OpenCV . Read a video stream from the webcam frame by frame using OpenCV in this third topic in the Data Science and Machine Learning Series. Performing Face Detection using OpenCV and the Haar Cascade Classifier . Perform face detection using OpenCV and the Haar Cascade Classifier in this fourth topic in the Data Science and Machine Learning Series. Generating the Face Recognition Training Dataset . Generate the face recognition training dataset in this fifth topic in the Data Science and Machine Learning Series. Follow along with Advait and extract images from the Webcam and detect faces and draw bounding boxes around each face. Applying the K-Nearest Neighbors Algorithm on the Iris Flower Dataset . Apply the K-Nearest Neighbors supervised learning algorithm on the Iris flower dataset for face recognition in this sixth topic in the Data Science and Machine Learning Series. Performing Face Recognition . Perform face recognition in this seventh topic in the Data Science and Machine Learning Series. Follow along with Advait and create a face recognition algorithm and test it by identifying images in a video stre...

Download or read book Facial Analysis from Continuous Video with Applications to Human Computer Interface written by Antonio J. Colmenarez and published by Springer Science & Business Media. This book was released on 2005-12-17 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision algorithms for the analysis of video data are obtained from a camera aimed at the user of an interactive system. It is potentially useful to enhance the interface between users and machines. These image sequences provide information from which machines can identify and keep track of their users, recognize their facial expressions and gestures, and complement other forms of human-computer interfaces. Facial Analysis from Continuous Video with Applications to Human-Computer Interfaces presents a learning technique based on information-theoretic discrimination which is used to construct face and facial feature detectors. This book also describes a real-time system for face and facial feature detection and tracking in continuous video. Finally, this book presents a probabilistic framework for embedded face and facial expression recognition from image sequences. Facial Analysis from Continuous Video with Applications to Human-Computer Interfaces is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.

Download or read book Advanced Visual Interfaces Supporting Artificial Intelligence and Big Data Applications written by Thoralf Reis and published by Springer Nature. This book was released on 2021-02-02 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-workshop proceedings of the AVI 2020 Workshop on Road Mapping Infrastructures for Artificial Intelligence Supporting Advanced Visual Big Data Analysis, AVI-BDA 2020, held in Ischia, Italy, in June 2020, and the Second Italian Workshop on Visualization and Visual Analytics, held in Ischia, Italy, in September 2020. The 14 regular papers in this volume present topics such as big data collection, management and curation; big data analytics; big data interaction and perception; big data insight and effectuation; configuration and management of big data storage and compute infrastructures, services, and tools; advanced visual interaction in big data applications; user empowerment and meta design in big data applications; prediction and automation of big data analysis workflows; as well as data visualization; information visualization; visual analytics; infographics; and design.

Download or read book Machine Intelligence and Emerging Technologies written by Md. Shahriare Satu and published by Springer Nature. This book was released on 2023-06-10 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNICST 490 and 491 constitutes the proceedings of the First International Conference on Machine Intelligence and Emerging Technologies, MIET 2022, hosted by Noakhali Science and Technology University, Noakhali, Bangladesh, during September 23–25, 2022. The 104 papers presented in the proceedings were carefully reviewed and selected from 272 submissions. This book focuses on theoretical, practical, state-of-art applications, and research challenges in the field of artificial intelligence and emerging technologies. It will be helpful for active researchers and practitioners in this field. These papers are organized in the following topical sections: imaging for disease detection; pattern recognition and natural language processing; bio signals and recommendation systems for wellbeing; network, security and nanotechnology; and emerging technologies for society and industry.

Download or read book Digital Architecture for Real time Face Detection for Deep Video Packet Inspection Systems written by Smrity Bhattarai and published by . This book was released on 2017 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Face detection and optional recognition is a highly researched area in digital image processing. Face detection allows gathering of statistical data from video sequences, with applications in a variety of areas such as bio-metrics, information security, and video surveillance. The growing abundance of video sensors that are connected to the internet require high-throughput real-time processing of a multitude of digital video feeds, where each feed provides independent real-time statistics of the numberof persons shown in the feed. Typical applications include pedestrian counting, public transit monitoring, crowd control, and sporting events. Video surveillance and security applications in particular can benefit from real-time algorithms that can process large amounts of data. Thousands of video sources must be monitored forextracting situational awareness information for homeland security and public safetyapplications, and the manual monitoring of such a vast amount of data is nearlyimpossible. Algorithms for both face detection and recognition take two main approaches involving the local detection of facial features based on a geometricmodel of the human face and a holistic based feature recognition, where the image data is treated as an entity without isolating different regions of the face. The main challenge in feature based facial detection is identification and location of human faces regardless of their pose, facial expression, orientation, imaging condition or presence of structural components. Some advanced image-based pattern recognition techniques have been developed to handle difficult scenarios like multiple faces, faces of different sizes, and even detection in heavily cluttered backgrounds. In this thesis, we explore how hardware computing architecture for detection of an image, as a face or non-face, is designed. The computing architecture is first designed, modeled, and tested in MATLAB simulink using Xilinx blockset. Images were later tested using a Virtex-6 FPGA ML605 Evaluation Kit. A field-programmable gate array (FPGA) is an integrated circuit designed to be configured by a user or a designer after manufacturing. The system uses the features of a face and non-face,which were previously extracted by training the set of face and non-face patterns. The system is fully feature based and does not require any assumptions for processing. In this approach, all the images are treated in the same way. They are not separated into different categories before processing them. The system is basically a combination of different modules like convolution, sub-sampling, bias add, scaling, neuron and decision combined in a specific format to classify the images as a face or non-face on the basis of the output. The algorithm is simple without any need for preprocessing of the image. The performance trade-off exists between the computational precision, chip area, clock speed, and power consumption.

Download or read book Face Recognition from Synchronous Videos written by Binglong Xie and published by . This book was released on 2006 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic face recognition has a lot of application areas, such as biometrics, information security, law enforcement, surveillance; smart cards and access control etc. Current single-camera face recognition has severe limitations when the subject is not cooperative, or there are pose changes and different illumination conditions. Face recognition using multiple synchronized cameras is proposed to overcome the limitations in this dissertation. The major contributions include illumination insensitive change detection, component-based single-channel face detection and recognition, and a fusion approach to effectively combine the multiple channels running the mentioned face detection and recognition. The illumination insensitive change detection is motivated from a statistical model derived from the physics of the imaging process, detects changes by examining the order consistency in the neighborhood that is resistant to sudden illumination changes, and can be used as a peripheral subsystem to work with face detection and recognition. The face detection is based on two levels of processing, in which the first level detects the face components using improved AdaBoost, and the second level fuses individual component detections to detect a face using a component face model. This approach is robust to face pose and illumination changes and fast compared to other face detection algorithms. The detected face is recognized by an LDA based face recognizer with fusion over time to further improve robustness. For the two-channel system, a reliability measure is effectively trained for each channel with features extracted from both face detection and recognition results, and it accurately chooses the more reliable channel as the final face recognition. The performance in terms of recognition rate is far better than that of either single channel, and it is consistently better than common classifier fusion rules. Future work will focus on joint similarity modeling to explore information hidden in all matching scores in all channels for better performance.

Download or read book Face Recognition written by Harry Wechsler and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: The NATO Advanced Study Institute (ASI) on Face Recognition: From Theory to Applications took place in Stirling, Scotland, UK, from June 23 through July 4, 1997. The meeting brought together 95 participants (including 18 invited lecturers) from 22 countries. The lecturers are leading researchers from academia, govemment, and industry from allover the world. The lecturers presented an encompassing view of face recognition, and identified trends for future developments and the means for implementing robust face recognition systems. The scientific programme consisted of invited lectures, three panels, and (oral and poster) presentations from students attending the AS!. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (i) human processing of face recognition and its relevance to forensic systems, (ii) face coding, (iii) connectionist methods and support vector machines (SVM), (iv) hybrid methods for face recognition, and (v) predictive learning and performance evaluation. The goals of the panels were to provide links among the lectures and to emphasis the themes of the meeting. The topics of the panels were: (i) How the human visual system processes faces, (ii) Issues in applying face recognition: data bases, evaluation and systems, and (iii) Classification issues involved in face recognition. The presentations made by students gave them an opportunity to receive feedback from the invited lecturers and suggestions for future work.

Download or read book Advances in Biometrics written by Seong-Whan Lee and published by Springer. This book was released on 2007-08-30 with total page 1234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Conference on Biometrics, ICB 2007, held in Seoul, Korea, August 2007. Biometric criteria covered by the papers are assigned to face, fingerprint, iris, speech and signature, biometric fusion and performance evaluation, gait, keystrokes, and others. In addition, the volume also announces the results of the Face Authentication Competition, FAC 2006.

Download or read book Real Time Face Matching with Multiple Cameras Using Principal Component Analysis written by Andrew Mullen and published by . This book was released on 2006 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Face recognition is a rapidly advancing research topic due to the large number of applications that can benefit from it. Face recognition consists of determining whether a known face is present in an image and is typically composed of four distinct steps. These steps are face detection, face alignment, feature extraction, and face classification. The leading application for face recognition is video surveillance. The majority of current research in face recognition has focused on determining if a face is present in an image, and if so, which subject in a known database is the closest match. This thesis deals with face matching, which is a subset of face recognition, focusing on face identification, yet it is an area where little research has been done. The objective of face matching is to determine, in real-time, the degree of confidence to which a live subject matches a facial image. Applications for face matching include video surveillance, determination of identification credentials, computer-human interfaces, and communications security. The method proposed here employs principal component analysis to create a method of face matching which is both computationally efficient and accurate. This method is integrated into a real time system that is based upon a two camera setup. it is able to scan the room, detect faces, and zoom in for a high quality capture of the facial features. The image capture is used in a face matching process to determine if the person found is the desired target. The performance of the system is analyzed based upon the matching accuracy for 10 unique subjects"--Abstract.

Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.