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"Human face recognition (Computer science)"
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Emotion recognition : a pattern analysis approach
A timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals This book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers. Written by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability. There is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems. Emotion Recognition: A Pattern Analysis Approach would be of great interest to researchers, graduate students and practitioners, as the book
* Offers both foundations and advances on emotion recognition in a single volume
* Provides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains
* Inspires young researchers to prepare themselves for their own research
* Demonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
eBook
Deep learning research applications for natural language processing
\"This book delves into issues of natural language processing, a subset of artificial intelligence that enables computers to understand the meaning of human language using techniques of machine learning and deep learning algorithms to discern a words' semantic meanings\"-- Provided by publisher.
BOOK
A systematic review on hand gesture recognition techniques, challenges and applications
With the development of today's technology, and as humans tend to naturally use hand gestures in their communication process to clarify their intentions, hand gesture recognition is considered to be an important part of Human Computer Interaction (HCI), which gives computers the ability of capturing and interpreting hand gestures, and executing commands afterwards. The aim of this study is to perform a systematic literature review for identifying the most prominent techniques, applications and challenges in hand gesture recognition.
To conduct this systematic review, we have screened 560 papers retrieved from IEEE Explore published from the year 2016 to 2018, in the searching process keywords such as \"hand gesture recognition\" and \"hand gesture techniques\" have been used. However, to focus the scope of the study 465 papers have been excluded. Only the most relevant hand gesture recognition works to the research questions, and the well-organized papers have been studied.
The results of this paper can be summarized as the following; the surface electromyography (sEMG) sensors with wearable hand gesture devices were the most acquisition tool used in the work studied, also Artificial Neural Network (ANN) was the most applied classifier, the most popular application was using hand gestures for sign language, the dominant environmental surrounding factor that affected the accuracy was the background color, and finally the problem of overfitting in the datasets was highly experienced.
The paper will discuss the gesture acquisition methods, the feature extraction process, the classification of hand gestures, the applications that were recently proposed, the challenges that face researchers in the hand gesture recognition process, and the future of hand gesture recognition. We shall also introduce the most recent research from the year 2016 to the year 2018 in the field of hand gesture recognition for the first time.
Journal Article
Social signal processing
\"Social Signal Processing is the first book to cover all aspects of the modeling, automated detection, analysis, and synthesis of nonverbal behavior in human-human and human-machine interactions. Authoritative surveys address conceptual foundations, machine analysis and synthesis of social signal processing, and applications. Foundational topics include affect perception and interpersonal coordination in communication; later chapters cover technologies for automatic detection and understanding such as computational paralinguistics and facial expression analysis and for the generation of artificial social signals such as social robots and artificial agents. The final section covers a broad spectrum of applications based on social signal processing in healthcare, deception detection, and digital cities, including detection of developmental diseases and analysis of small groups. Each chapter offers a basic introduction to its topic, accessible to students and other newcomers, and then outlines challenges and future perspectives for the benefit of experienced researchers and practitioners in the field\"-- Provided by publisher.
Book
Realistic Speech-Driven Facial Animation with GANs
Speech-driven facial animation is the process that automatically synthesizes talking characters based on speech signals. The majority of work in this domain creates a mapping from audio features to visual features. This approach often requires post-processing using computer graphics techniques to produce realistic albeit subject dependent results. We present an end-to-end system that generates videos of a talking head, using only a still image of a person and an audio clip containing speech, without relying on handcrafted intermediate features. Our method generates videos which have (a) lip movements that are in sync with the audio and (b) natural facial expressions such as blinks and eyebrow movements. Our temporal GAN uses 3 discriminators focused on achieving detailed frames, audio-visual synchronization, and realistic expressions. We quantify the contribution of each component in our model using an ablation study and we provide insights into the latent representation of the model. The generated videos are evaluated based on sharpness, reconstruction quality, lip-reading accuracy, synchronization as well as their ability to generate natural blinks.
Journal Article
Your face belongs to us : a secretive startup's quest to end privacy as we know it
\"In this riveting feat of reporting, Kashmir Hill illuminates the improbable rise of Clearview AI and how Hoan Ton-That, a computer engineer and Richard Schwartz, a Giuliani associate, launched a terrifying facial recognition app with society-altering potential. They were assisted by a cast of controversial characters, including conservative provocateur Charles Johnson and billionaire Trump backer Peter Thiel. The app can scan a blurry portrait, and, in just seconds, collect every instance of a person's online life. It can find your name, your social media profiles, your friends and family, even your home address (as well as photos of you that you may not even have known existed). The story of Clearview AI opens up a window into a larger, more urgent one about our tortured relationship to technology, the way it entertains and seduces us even as it steals our privacy and lays us bare to bad actors in politics, criminal justice, and tech. This technology has been quietly growing more powerful for decades. Ubiquitous in China and Russia, it was also developed by American companies, including Google and Facebook, who decided it was too radical to release. That did not stop Clearview. They gave demos of the tech to interested private investors and contracted it out to hundreds of law enforcement agencies around the country. American law enforcement, including the Department of Homeland Security, has already used it to arrest people for everything from petty theft to assault. Without regulation it could expand the reach of policing-as it has in China and Russia-to a terrifying, dystopian level\"-- Provided by publisher.
Book
Face detection techniques: a review
With the marvelous increase in video and image database there is an incredible need of automatic understanding and examination of information by the intelligent systems as manually it is getting to be plainly distant. Face plays a major role in social intercourse for conveying identity and feelings of a person. Human beings have not tremendous ability to identify different faces than machines. So, automatic face detection system plays an important role in face recognition, facial expression recognition, head-pose estimation, human–computer interaction etc. Face detection is a computer technology that determines the location and size of a human face in a digital image. Face detection has been a standout amongst topics in the computer vision literature. This paper presents a comprehensive survey of various techniques explored for face detection in digital images. Different challenges and applications of face detection are also presented in this paper. At the end, different standard databases for face detection are also given with their features. Furthermore, we organize special discussions on the practical aspects towards the development of a robust face detection system and conclude this paper with several promising directions for future research.
Journal Article
A review on the long short-term memory model
Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. According to several online sources, this model has improved Google’s speech recognition, greatly improved machine translations on Google Translate, and the answers of Amazon’s Alexa. This neural system is also employed by Facebook, reaching over 4 billion LSTM-based translations per day as of 2017. Interestingly, recurrent neural networks had shown a rather discrete performance until LSTM showed up. One reason for the success of this recurrent network lies in its ability to handle the exploding/vanishing gradient problem, which stands as a difficult issue to be circumvented when training recurrent or very deep neural networks. In this paper, we present a comprehensive review that covers LSTM’s formulation and training, relevant applications reported in the literature and code resources implementing this model for a toy example.
Journal Article
Face detection in still images under occlusion and non-uniform illumination
Face detection is important part of face recognition system. In face recognition, face detection is taken not so seriously. Face detection is taken for granted; primarily focus is on face recognition. Also, many challenges associated with face detection, increases the value of TN (True Negative). A lot of work has been done in field of face recognition. But in field of face detection, especially with problems of face occlusion and non-uniform illumination, not so much work has been done. It directly affects the efficiency of applications linked with face detection, example face recognition, surveillance, etc. So, these reasons motivate us to do research in field of face detection, especially with problems of face occlusion and non-uniform illumination. The main objective of this article is to detect face in still image. Experimental work has been conducted on images having problem of face occlusion and non-uniform illumination. Experimental images have been taken from public dataset AR face dataset and Color FERET dataset. One manual dataset has also been created for experimental purpose. The images in this manual dataset have been taken from the internet. This involves making the machine intelligent enough to acquire the human perception and knowledge to detect, localize and recognize the face in an arbitrary image with the same ease as humans do it. This article proposes an efficient technique for face detection from still images under occlusion and non-uniform illumination. The authors have presented a face detection technique using a combination of YCbCr, HSV and L × a × b color model. The proposed technique improved results in terms of Accuracy, Detection Rate, False Detection Rate and Precision. This technique can be useful in the surveillance and security related applications.
Journal Article