End-to-end 3D face reconstruction with deep neural networks

Monocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB image and an initial 3D facial shape rendering, our DNN model is end-to-end, and thus the complicated 3D rendering process can be avoided. Moreover, we integrate in the DNN architecture two components, namely a multi-task loss function and a fusion convolutional neural network (CNN) to improve facial expression reconstruction. With the multi-task loss function, 3D face reconstruction is divided into neutral 3D facial shape reconstruction and expressive 3D facial shape reconstruction. The neutral 3D facial shape is class-specific. Therefore, higher layer features are useful. In comparison, the expressive 3D facial shape favors lower or intermediate layer features. With the fusion-CNN, features from different intermediate layers are fused and transformed for predicting the 3D expressive facial shape. Through extensive experiments, we demonstrate the superiority of our end-to-end framework in improving the accuracy of 3D face reconstruction.Comment: Accepted to CVPR1

A comprehensive survey on Pose-Invariant Face Recognition

© 2016 ACM. The capacity to recognize faces under varied poses is a fundamental human ability that presents a unique challenge for computer vision systems. Compared to frontal face recognition, which has been intensively studied and has gradually matured in the past few decades, Pose-Invariant Face Recognition (PIFR) remains a largely unsolved problem. However, PIFR is crucial to realizing the full potential of face recognition for real-world applications, since face recognition is intrinsically a passive biometric technology for recognizing uncooperative subjects. In this article, we discuss the inherent difficulties in PIFR and present a comprehensive review of established techniques. Existing PIFR methods can be grouped into four categories, that is, pose-robust feature extraction approaches, multiview subspace learning approaches, face synthesis approaches, and hybrid approaches. The motivations, strategies, pros/cons, and performance of representative approaches are described and compared. Moreover, promising directions for future research are discussed

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

The Role of Emotional and Facial Expression in Synthesised Sign Language Avatars

This thesis explores the role that underlying emotional facial expressions might have in regards to understandability in sign language avatars. Focusing specifically on Irish Sign Language (ISL), we examine the Deaf community’s requirement for a visual-gestural language as well as some linguistic attributes of ISL which we consider fundamental to this research. Unlike spoken language, visual-gestural languages such as ISL have no standard written representation. Given this, we compare current methods of written representation for signed languages as we consider: which, if any, is the most suitable transcription method for the medical receptionist dialogue corpus. A growing body of work is emerging from the field of sign language avatar synthesis. These works are now at a point where they can benefit greatly from introducing methods currently used in the field of humanoid animation and, more specifically, the application of morphs to represent facial expression. The hypothesis underpinning this research is: augmenting an existing avatar (eSIGN) with various combinations of the 7 widely accepted universal emotions identified by Ekman (1999) to deliver underlying facial expressions, will make that avatar more human-like. This research accepts as true that this is a factor in improving usability and understandability for ISL users. Using human evaluation methods (Huenerfauth, et al., 2008) the research compares an augmented set of avatar utterances against a baseline set with regards to 2 key areas: comprehension and naturalness of facial configuration. We outline our approach to the evaluation including our choice of ISL participants, interview environment, and evaluation methodology. Remarkably, the results of this manual evaluation show that there was very little difference between the comprehension scores of the baseline avatars and those augmented withEFEs. However, after comparing the comprehension results for the synthetic human avatar “Anna” against the caricature type avatar “Luna”, the synthetic human avatar Anna was the clear winner. The qualitative feedback allowed us an insight into why comprehension scores were not higher in each avatar and we feel that this feedback will be invaluable to the research community in the future development of sign language avatars. Other questions asked in the evaluation focused on sign language avatar technology in a more general manner. Significantly, participant feedback in regard to these questions indicates a rise in the level of literacy amongst Deaf adults as a result of mobile technology

Data and methods for a visual understanding of sign languages

Signed languages are complete and natural languages used as the first or preferred mode of communication by millions of people worldwide. However, they, unfortunately, continue to be marginalized languages. Designing, building, and evaluating models that work on sign languages presents compelling research challenges and requires interdisciplinary and collaborative efforts. The recent advances in Machine Learning (ML) and Artificial Intelligence (AI) has the power to enable better accessibility to sign language users and narrow down the existing communication barrier between the Deaf community and non-sign language users. However, recent AI-powered technologies still do not account for sign language in their pipelines. This is mainly because sign languages are visual languages, that use manual and non-manual features to convey information, and do not have a standard written form. Thus, the goal of this thesis is to contribute to the development of new technologies that account for sign language by creating large-scale multimodal resources suitable for training modern data-hungry machine learning models and developing automatic systems that focus on computer vision tasks related to sign language that aims at learning better visual understanding of sign languages. Thus, in Part I, we introduce the How2Sign dataset, which is a large-scale collection of multimodal and multiview sign language videos in American Sign Language. In Part II, we contribute to the development of technologies that account for sign languages by presenting in Chapter 4 a framework called Spot-Align, based on sign spotting methods, to automatically annotate sign instances in continuous sign language. We further present the benefits of this framework and establish a baseline for the sign language recognition task on the How2Sign dataset. In addition to that, in Chapter 5 we benefit from the different annotations and modalities of the How2Sign to explore sign language video retrieval by learning cross-modal embeddings. Later in Chapter 6, we explore sign language video generation by applying Generative Adversarial Networks to the sign language domain and assess if and how well sign language users can understand automatically generated sign language videos by proposing an evaluation protocol based on How2Sign topics and English translationLes llengües de signes són llengües completes i naturals que utilitzen milions de persones de tot el món com mode de comunicació primer o preferit. Tanmateix, malauradament, continuen essent llengües marginades. Dissenyar, construir i avaluar tecnologies que funcionin amb les llengües de signes presenta reptes de recerca que requereixen d’esforços interdisciplinaris i col·laboratius. Els avenços recents en l’aprenentatge automàtic i la intel·ligència artificial (IA) poden millorar l’accessibilitat tecnològica dels signants, i alhora reduir la barrera de comunicació existent entre la comunitat sorda i les persones no-signants. Tanmateix, les tecnologies més modernes en IA encara no consideren les llengües de signes en les seves interfícies amb l’usuari. Això es deu principalment a que les llengües de signes són llenguatges visuals, que utilitzen característiques manuals i no manuals per transmetre informació, i no tenen una forma escrita estàndard. Els objectius principals d’aquesta tesi són la creació de recursos multimodals a gran escala adequats per entrenar models d’aprenentatge automàtic per a llengües de signes, i desenvolupar sistemes de visió per computador adreçats a una millor comprensió automàtica de les llengües de signes. Així, a la Part I presentem la base de dades How2Sign, una gran col·lecció multimodal i multivista de vídeos de la llengua de signes nord-americana. A la Part II, contribuïm al desenvolupament de tecnologia per a llengües de signes, presentant al capítol 4 una solució per anotar signes automàticament anomenada Spot-Align, basada en mètodes de localització de signes en seqüències contínues de signes. Després, presentem els avantatges d’aquesta solució i proporcionem uns primers resultats per la tasca de reconeixement de la llengua de signes a la base de dades How2Sign. A continuació, al capítol 5 aprofitem de les anotacions i diverses modalitats de How2Sign per explorar la cerca de vídeos en llengua de signes a partir de l’entrenament d’incrustacions multimodals. Finalment, al capítol 6, explorem la generació de vídeos en llengua de signes aplicant xarxes adversàries generatives al domini de la llengua de signes. Avaluem fins a quin punt els signants poden entendre els vídeos generats automàticament, proposant un nou protocol d’avaluació basat en les categories dins de How2Sign i la traducció dels vídeos a l’anglès escritLas lenguas de signos son lenguas completas y naturales que utilizan millones de personas de todo el mundo como modo de comunicación primero o preferido. Sin embargo, desgraciadamente, siguen siendo lenguas marginadas. Diseñar, construir y evaluar tecnologías que funcionen con las lenguas de signos presenta retos de investigación que requieren esfuerzos interdisciplinares y colaborativos. Los avances recientes en el aprendizaje automático y la inteligencia artificial (IA) pueden mejorar la accesibilidad tecnológica de los signantes, al tiempo que reducir la barrera de comunicación existente entre la comunidad sorda y las personas no signantes. Sin embargo, las tecnologías más modernas en IA todavía no consideran las lenguas de signos en sus interfaces con el usuario. Esto se debe principalmente a que las lenguas de signos son lenguajes visuales, que utilizan características manuales y no manuales para transmitir información, y carecen de una forma escrita estándar. Los principales objetivos de esta tesis son la creación de recursos multimodales a gran escala adecuados para entrenar modelos de aprendizaje automático para lenguas de signos, y desarrollar sistemas de visión por computador dirigidos a una mejor comprensión automática de las lenguas de signos. Así, en la Parte I presentamos la base de datos How2Sign, una gran colección multimodal y multivista de vídeos de lenguaje la lengua de signos estadounidense. En la Part II, contribuimos al desarrollo de tecnología para lenguas de signos, presentando en el capítulo 4 una solución para anotar signos automáticamente llamada Spot-Align, basada en métodos de localización de signos en secuencias continuas de signos. Después, presentamos las ventajas de esta solución y proporcionamos unos primeros resultados por la tarea de reconocimiento de la lengua de signos en la base de datos How2Sign. A continuación, en el capítulo 5 aprovechamos de las anotaciones y diversas modalidades de How2Sign para explorar la búsqueda de vídeos en lengua de signos a partir del entrenamiento de incrustaciones multimodales. Finalmente, en el capítulo 6, exploramos la generación de vídeos en lengua de signos aplicando redes adversarias generativas al dominio de la lengua de signos. Evaluamos hasta qué punto los signantes pueden entender los vídeos generados automáticamente, proponiendo un nuevo protocolo de evaluación basado en las categorías dentro de How2Sign y la traducción de los vídeos al inglés escrito.Teoria del Senyal i Comunicacion