Perceptual quality assessment and processing for visual signals.

視覺信號，包括圖像，視頻等，在采集，壓縮，存儲，傳輸，重新生成的過程中都會被各種各樣的噪聲所影響，因此他們的主觀質量也就會降低。所以，主觀視覺質量在現今的視覺信號處理跟通訊系統中起到了很大的作用。這篇畢業論文主要討論質量評價的算法設計，以及這些衡量標準在視覺信號處理上的應用。這篇論文的工作主要包括以下五個方面。第一部分主要集中在具有完全套考原始圖像的圖像質量評價。首先我們研究人類視覺系統的特征。具體說來，視覺在結構化失真上面的水平特性和顯著特征會被建模然后應用到結構相似度(SSIM)這個衡量標準上。實驗顯示我們的方法明顯的提高了衡量標準典主觀評價的相似度。由這個質量衡量標準的啟發，我們設計了一個主觀圖像壓縮的方法。其中我們提出了一個自適應的塊大小的超分辨率算法指導的下采樣的算法。實驗結果證明提出的圖像壓縮算法無論在主觀還是在客觀層面都構建了高質量的圖像。第二個部分的工作主要討論具有完全參考原始視頻的視頻質量評價。考慮到人類視覺系統的特征，比如時空域的對此敏感函數，眼球的移動，紋理的遮掩特性，空間域的一致性，時間域的協調性，不同塊變換的特性，我們設計了一個自適應塊大小的失真閾值的模型。實驗證明，我們提出的失真閾值模型能夠更精確的描迷人類視覺系統的特性。基于這個自適應塊大小的失真閾值模型，我們設計了一個簡單的主觀質量評價標準。在公共的圓像以及視頻的主觀數據庫上的測試結果證明了這個簡單的評價標準的有效性。因此，我們把這個簡單的質量標準應用于視頻編碼系統中。它可以在同樣的碼率下提供更高主觀質量的視頻。第三部分我們討論具有部分參考信息的圖像質量評價。我們通過描迷重組后的離散余弦變換域的系數的統計分布來衡量圖像的主觀質量。提出的評價標準發掘了相鄰的離散余弦系數的相同統計特性，相鄰的重組離散余弦系數的互信息，以及圖像的能量在不同頻率下的分布。實驗結果證明我們提出的質量標準河以超越其他的具有部分參考信息的質量評價標準，甚至還超過了具有完全參考信息的質量評價標準。而且，提取的特征很容易被編碼以及隱藏到圖像中以便于在圖像通訊中進行質量監控。第四部分我們討論具有部分參考信息的視頻質量評價。我們提取的特征可以很好的描迷空間域的信息失，和時間域的相鄰兩幀間的直方圖的統計特性。在視頻主觀質量的數據庫上的實驗結果，也證明了提出的方法河以超越其他代表性的視頻質量評價標準，甚至是具有完全參考信息的質量評價標準， 譬如PSNR以及SSIM 。我們的方法只需要很少的特征來描迷每一幀視頻圖像。對于每一幀圖像，一個特征用于描迷空間域的特點，另外三個特征用于描述時間域的特點。考慮到計算的復雜度以及壓縮特征所需要的碼率，提出的方法河以很簡單的在視頻的傳輸過程中監控視頻的質量。之前的四部分提到的主觀質量評價標準主要集中在傳統的失真上面， 譬如JPEG 圖像壓縮， H.264視頻壓縮。在最后一部分，我們討論在圖像跟視頻的retargeting過程中的失真。現如今，隨著消費者電子的發展，視覺信號需要在不同分辨率的顯示設備上進行通訊交互。因此， retargeting的算法把同一個原始圖像適應于不同的分辨率的顯示設備。這樣的過程就會引入圖像的失真。我們研究了對于retargeting圖像主觀質量的測試者的分數，從三個方面進行討論測試者對于retargeting圖像失真的反應.圖像retargeting的尺度，圖像retargeting的算法，原始圖像的內容特性。通過大量的主觀實驗測試，我們構建了一個關于圖像retargeting的主觀數據庫。基于這個主觀數據庫，我們評價以及分析了幾個具有代表性的質量評價標準。Visual signals, including images, videos, etc., are affected by a wide variety of distortions during acquisition, compression, storage, processing, transmission, and reproduction processes, which result in perceptual quality degradation. As a result, perceptual quality assessment plays a very important role in today's visual signal processing and communication systems. In this thesis, quality assessment algorithms for evaluating the visual signal perceptual quality, as well as the applications on visual signal processing and communications, are investigated. The work consists of five parts as briefly summarized below.The first part focuses on the full-reference (FR) image quality assessment. The properties of the human visual system (HVS) are firstly investigated. Specifically, the visual horizontal effect (HE) and saliency properties over the structural distortions are modelled and incorporated into the structure similarity index (SSIM). Experimental results show significantly improved performance in matching the subjective ratings. Inspired by the developed FR image metric, a perceptual image compression scheme is developed, where the adaptive block-based super-resolution directed down-sampling is proposed. Experimental results demonstrated that the proposed image compression scheme can produce higher quality images in terms of both objective and subjective qualities, compared with the existing methods.The second part concerns the FR video quality assessment. The adaptive block-size transform (ABT) based just-noticeable difference (JND) for visual signals is investigated by considering the HVS characteristics, e.g., spatio-temporal contrast sensitivity function (CSF), eye movement, texture masking, spatial coherence, temporal consistency, properties of different block-size transforms, etc. It is verified that the developed ABT based JND can more accurately depict the HVS property, compared with the state-of-the-art JND models. The ABT based JND is thereby utilized to develop a simple perceptual quality metric for visual signals. Validations on the image and video subjective quality databases proved its effectiveness. As a result, the developed perceptual quality metric is employed for perceptual video coding, which can deliver video sequences of higher perceptual quality at the same bit-rates.The third part discusses the reduced-reference (RR) image quality assessment, which is developed by statistically modelling the coe cient distribution in the reorganized discrete cosine transform (RDCT) domain. The proposed RR metric exploits the identical statistical nature of the adjacent DCT coefficients, the mutual information (MI) relationship between adjacent RDCT coefficients, and the image energy distribution among different frequency components. Experimental results demonstrate that the proposed metric outperforms the representative RR image quality metrics, and even the FR quality metric, i.e., peak signal to noise ratio (PSNR). Furthermore, the extracted RR features can be easily encoded and embedded into the distorted images for quality monitoring during image communications.The fourth part investigates the RR video quality assessment. The RR features are extracted to exploit the spatial information loss and the temporal statistical characteristics of the inter-frame histogram. Evaluations on the video subjective quality databases demonstrate that the proposed method outperforms the representative RR video quality metrics, and even the FR metrics, such as PSNR, SSIM in matching the subjective ratings. Furthermore, only a small number of RR features is required to represent the original video sequence (each frame requires only 1 and 3 parameters to depict the spatial and temporal characteristics, respectively). By considering the computational complexity and the bit-rates for extracting and representing the RR features, the proposed RR quality metric can be utilized for quality monitoring during video transmissions, where the RR features for perceptual quality analysis can be easily embedded into the videos or transmitted through an ancillary data channel.The aforementioned perceptual quality metrics focus on the traditional distortions, such as JPEG image compression noise, H.264 video compression noise, and so on. In the last part, we investigate the distortions introduced during the image and video retargeting process. Nowadays, with the development of the consumer electronics, more and more visual signals have to communicate between different display devices of different resolutions. The retargeting algorithm is employed to adapt a source image of one resolution to be displayed in a device of a different resolution, which may introduce distortions during the retargeting process. We investigate the subjective responses on the perceptual qualities of the retargeted images, and discuss the subjective results from three perspectives, i.e., retargeting scales, retargeting methods, and source image content attributes. An image retargeting subjective quality database is built by performing a large-scale subjective study of image retargeting quality on a collection of retargeted images. Based on the built database, several representative quality metrics for retargeted images are evaluated and discussed.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Detailed summary in vernacular field only.Ma, Lin."December 2012."Thesis (Ph.D.)--Chinese University of Hong Kong, 2013.Includes bibliographical references (leaves 185-197).Abstract also in Chinese.Dedication --- p.iiAcknowledgments --- p.iiiAbstract --- p.viiiPublications --- p.xiNomenclature --- p.xviiContents --- p.xxivList of Figures --- p.xxviiiList of Tables --- p.xxxChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation and Objectives --- p.1Chapter 1.2 --- Subjective Perceptual Quality Assessment --- p.5Chapter 1.3 --- Objective Perceptual Quality Assessment --- p.10Chapter 1.3.1 --- Visual Modelling Approach --- p.10Chapter 1.3.2 --- Engineering Modelling Approach --- p.15Chapter 1.3.3 --- Perceptual Subjective Quality Databases --- p.19Chapter 1.3.4 --- Performance Evaluation --- p.21Chapter 1.4 --- Thesis Contributions --- p.22Chapter 1.5 --- Organization of the Thesis --- p.24Chapter I --- Full Reference Quality Assessment --- p.26Chapter 2 --- Full Reference Image Quality Assessment --- p.27Chapter 2.1 --- Visual Horizontal Effect for Image Quality Assessment --- p.27Chapter 2.1.1 --- Introduction --- p.27Chapter 2.1.2 --- Proposed Image Quality Assessment Framework --- p.28Chapter 2.1.3 --- Experimental Results --- p.34Chapter 2.1.4 --- Conclusion --- p.36Chapter 2.2 --- Image Compression via Adaptive Block-Based Super-Resolution Directed Down-Sampling --- p.37Chapter 2.2.1 --- Introduction --- p.37Chapter 2.2.2 --- The Proposed Image Compression Framework --- p.38Chapter 2.2.3 --- Experimental Results --- p.42Chapter 2.2.4 --- Conclusion --- p.45Chapter 3 --- Full Reference Video Quality Assessment --- p.46Chapter 3.1 --- Adaptive Block-size Transform based Just-Noticeable Dfference Model for Visual Signals --- p.46Chapter 3.1.1 --- Introduction --- p.46Chapter 3.1.2 --- JND Model based on Transforms of Different Block Sizes --- p.48Chapter 3.1.3 --- Selection Strategy Between Transforms of Different Block Sizes --- p.53Chapter 3.1.4 --- JND Model Evaluation --- p.56Chapter 3.1.5 --- Conclusion --- p.60Chapter 3.2 --- Perceptual Quality Assessment --- p.60Chapter 3.2.1 --- Experimental Results --- p.62Chapter 3.2.2 --- Conclusion --- p.64Chapter 3.3 --- Motion Trajectory Based Visual Saliency for Video Quality Assessment --- p.65Chapter 3.3.1 --- Motion Trajectory based Visual Saliency for VQA --- p.66Chapter 3.3.2 --- New Quaternion Representation (QR) for Each frame --- p.66Chapter 3.3.3 --- Saliency Map Construction by QR --- p.67Chapter 3.3.4 --- Incorporating Visual Saliency with VQAs --- p.68Chapter 3.3.5 --- Experimental Results --- p.69Chapter 3.3.6 --- Conclusion --- p.72Chapter 3.4 --- Perceptual Video Coding --- p.72Chapter 3.4.1 --- Experimental Results --- p.75Chapter 3.4.2 --- Conclusion --- p.76Chapter II --- Reduced Reference Quality Assessment --- p.77Chapter 4 --- Reduced Reference Image Quality Assessment --- p.78Chapter 4.1 --- Introduction --- p.78Chapter 4.2 --- Reorganization Strategy of DCT Coefficients --- p.81Chapter 4.3 --- Relationship Analysis of Intra and Inter RDCT subbands --- p.83Chapter 4.4 --- Reduced Reference Feature Extraction in Sender Side --- p.88Chapter 4.4.1 --- Intra RDCT Subband Modeling --- p.89Chapter 4.4.2 --- Inter RDCT Subband Modeling --- p.91Chapter 4.4.3 --- Image Frequency Feature --- p.92Chapter 4.5 --- Perceptual Quality Analysis in the Receiver Side --- p.95Chapter 4.5.1 --- Intra RDCT Feature Difference Analysis --- p.95Chapter 4.5.2 --- Inter RDCT Feature Difference Analysis --- p.96Chapter 4.5.3 --- Image Frequency Feature Difference Analysis --- p.96Chapter 4.6 --- Experimental Results --- p.98Chapter 4.6.1 --- Efficiency of the DCT Reorganization Strategy --- p.98Chapter 4.6.2 --- Performance of the Proposed RR IQA --- p.99Chapter 4.6.3 --- Performance of the Proposed RR IQA over Each Individual Distortion Type --- p.105Chapter 4.6.4 --- Statistical Significance --- p.107Chapter 4.6.5 --- Performance Analysis of Each Component --- p.109Chapter 4.7 --- Conclusion --- p.111Chapter 5 --- Reduced Reference Video Quality Assessment --- p.113Chapter 5.1 --- Introduction --- p.113Chapter 5.2 --- Proposed Reduced Reference Video Quality Metric --- p.114Chapter 5.2.1 --- Reduced Reference Feature Extraction from Spatial Perspective --- p.116Chapter 5.2.2 --- Reduced Reference Feature Extraction from Temporal Perspective --- p.118Chapter 5.2.3 --- Visual Quality Analysis in Receiver Side --- p.121Chapter 5.3 --- Experimental Results --- p.123Chapter 5.3.1 --- Consistency Test of the Proposed RR VQA over Compressed Video Sequences --- p.124Chapter 5.3.2 --- Consistency Test of the Proposed RR VQA over Video Sequences with Simulated Distortions --- p.126Chapter 5.3.3 --- Performance Evaluation of the Proposed RR VQA on Compressed Video Sequences --- p.129Chapter 5.3.4 --- Performance Evaluation of the Proposed RR VQA on Video Sequences Containing Transmission Distortions --- p.133Chapter 5.3.5 --- Performance Analysis of Each Component --- p.135Chapter 5.4 --- Conclusion --- p.137Chapter III --- Retargeted Visual Signal Quality Assessment --- p.138Chapter 6 --- Image Retargeting Perceptual Quality Assessment --- p.139Chapter 6.1 --- Introduction --- p.139Chapter 6.2 --- Preparation of Database Building --- p.142Chapter 6.2.1 --- Source Image --- p.142Chapter 6.2.2 --- Retargeting Methods --- p.143Chapter 6.2.3 --- Subjective Testing --- p.146Chapter 6.3 --- Data Processing and Analysis for the Database --- p.150Chapter 6.3.1 --- Processing of Subjective Ratings --- p.150Chapter 6.3.2 --- Analysis and Discussion of the Subjective Ratings --- p.153Chapter 6.4 --- Objective Quality Metric for Retargeted Images --- p.162Chapter 6.4.1 --- Quality Metric Performances on the Constructed Image Retargeting Database --- p.162Chapter 6.4.2 --- Subjective Analysis of the Shape Distortion and Content Information Loss --- p.165Chapter 6.4.3 --- Discussion --- p.167Chapter 6.5 --- Conclusion --- p.169Chapter 7 --- Conclusions --- p.170Chapter 7.1 --- Conclusion --- p.170Chapter 7.2 --- Future Work --- p.173Chapter A --- Attributes of the Source Image --- p.176Chapter B --- Retargeted Image Name and the Corresponding Number --- p.179Chapter C --- Source Image Name and the Corresponding Number --- p.183Bibliography --- p.18

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Towards gestural understanding for intelligent robots

Fritsch JN. Towards gestural understanding for intelligent robots. Bielefeld: Universität Bielefeld; 2012.A strong driving force of scientific progress in the technical sciences is the quest for systems that assist humans in their daily life and make their life easier and more enjoyable. Nowadays smartphones are probably the most typical instances of such systems. Another class of systems that is getting increasing attention are intelligent robots. Instead of offering a smartphone touch screen to select actions, these systems are intended to offer a more natural human-machine interface to their users. Out of the large range of actions performed by humans, gestures performed with the hands play a very important role especially when humans interact with their direct surrounding like, e.g., pointing to an object or manipulating it. Consequently, a robot has to understand such gestures to offer an intuitive interface. Gestural understanding is, therefore, a key capability on the way to intelligent robots. This book deals with vision-based approaches for gestural understanding. Over the past two decades, this has been an intensive field of research which has resulted in a variety of algorithms to analyze human hand motions. Following a categorization of different gesture types and a review of other sensing techniques, the design of vision systems that achieve hand gesture understanding for intelligent robots is analyzed. For each of the individual algorithmic steps – hand detection, hand tracking, and trajectory-based gesture recognition – a separate Chapter introduces common techniques and algorithms and provides example methods. The resulting recognition algorithms are considering gestures in isolation and are often not sufficient for interacting with a robot who can only understand such gestures when incorporating the context like, e.g., what object was pointed at or manipulated. Going beyond a purely trajectory-based gesture recognition by incorporating context is an important prerequisite to achieve gesture understanding and is addressed explicitly in a separate Chapter of this book. Two types of context, user-provided context and situational context, are reviewed and existing approaches to incorporate context for gestural understanding are reviewed. Example approaches for both context types provide a deeper algorithmic insight into this field of research. An overview of recent robots capable of gesture recognition and understanding summarizes the currently realized human-robot interaction quality. The approaches for gesture understanding covered in this book are manually designed while humans learn to recognize gestures automatically during growing up. Promising research targeted at analyzing developmental learning in children in order to mimic this capability in technical systems is highlighted in the last Chapter completing this book as this research direction may be highly influential for creating future gesture understanding systems

Quality of Experience in Immersive Video Technologies

Over the last decades, several technological revolutions have impacted the television industry, such as the shifts from black & white to color and from standard to high-definition. Nevertheless, further considerable improvements can still be achieved to provide a better multimedia experience, for example with ultra-high-definition, high dynamic range & wide color gamut, or 3D. These so-called immersive technologies aim at providing better, more realistic, and emotionally stronger experiences. To measure quality of experience (QoE), subjective evaluation is the ultimate means since it relies on a pool of human subjects. However, reliable and meaningful results can only be obtained if experiments are properly designed and conducted following a strict methodology. In this thesis, we build a rigorous framework for subjective evaluation of new types of image and video content. We propose different procedures and analysis tools for measuring QoE in immersive technologies. As immersive technologies capture more information than conventional technologies, they have the ability to provide more details, enhanced depth perception, as well as better color, contrast, and brightness. To measure the impact of immersive technologies on the viewersâ QoE, we apply the proposed framework for designing experiments and analyzing collected subjectsâ ratings. We also analyze eye movements to study human visual attention during immersive content playback. Since immersive content carries more information than conventional content, efficient compression algorithms are needed for storage and transmission using existing infrastructures. To determine the required bandwidth for high-quality transmission of immersive content, we use the proposed framework to conduct meticulous evaluations of recent image and video codecs in the context of immersive technologies. Subjective evaluation is time consuming, expensive, and is not always feasible. Consequently, researchers have developed objective metrics to automatically predict quality. To measure the performance of objective metrics in assessing immersive content quality, we perform several in-depth benchmarks of state-of-the-art and commonly used objective metrics. For this aim, we use ground truth quality scores, which are collected under our subjective evaluation framework. To improve QoE, we propose different systems for stereoscopic and autostereoscopic 3D displays in particular. The proposed systems can help reducing the artifacts generated at the visualization stage, which impact picture quality, depth quality, and visual comfort. To demonstrate the effectiveness of these systems, we use the proposed framework to measure viewersâ preference between these systems and standard 2D & 3D modes. In summary, this thesis tackles the problems of measuring, predicting, and improving QoE in immersive technologies. To address these problems, we build a rigorous framework and we apply it through several in-depth investigations. We put essential concepts of multimedia QoE under this framework. These concepts not only are of fundamental nature, but also have shown their impact in very practical applications. In particular, the JPEG, MPEG, and VCEG standardization bodies have adopted these concepts to select technologies that were proposed for standardization and to validate the resulting standards in terms of compression efficiency

Merging the Real and the Virtual: An Exploration of Interaction Methods to Blend Realities

We investigate, build, and design interaction methods to merge the real with the virtual. An initial investigation looks at spatial augmented reality (SAR) and its effects on pointing with a real mobile phone. A study reveals a set of trade-offs between the raycast, viewport, and direct pointing techniques. To further investigate the manipulation of virtual content within a SAR environment, we design an interaction technique that utilizes the distance that a user holds mobile phone away from their body. Our technique enables pushing virtual content from a mobile phone to an external SAR environment, interact with that content, rotate-scale-translate it, and pull the content back into the mobile phone. This is all done in a way that ensures seamless transitions between the real environment of the mobile phone and the virtual SAR environment. To investigate the issues that occur when the physical environment is hidden by a fully immersive virtual reality (VR) HMD, we design and investigate a system that merges a realtime 3D reconstruction of the real world with a virtual environment. This allows users to freely move, manipulate, observe, and communicate with people and objects situated in their physical reality without losing their sense of immersion or presence inside a virtual world. A study with VR users demonstrates the affordances provided by the system and how it can be used to enhance current VR experiences. We then move to AR, to investigate the limitations of optical see-through HMDs and the problem of communicating the internal state of the virtual world with unaugmented users. To address these issues and enable new ways to visualize, manipulate, and share virtual content, we propose a system that combines a wearable SAR projector. Demonstrations showcase ways to utilize the projected and head-mounted displays together, such as expanding field of view, distributing content across depth surfaces, and enabling bystander collaboration. We then turn to videogames to investigate how spectatorship of these virtual environments can be enhanced through expanded video rendering techniques. We extract and combine additional data to form a cumulative 3D representation of the live game environment for spectators, which enables each spectator to individually control a personal view into the stream while in VR. A study shows that users prefer spectating in VR when compared with a comparable desktop rendering

Exploring images with deep learning for classification, retrieval and synthesis

In 2018, the number of mobile phone users will reach about 4.9 billion. Assuming an average of 5 photos taken per day using the built-in cameras would result in about 9 trillion photos annually. Thus, it becomes challenging to mine semantic information from such a huge amount of visual data. To solve this challenge, deep learning, an important sub-field in machine learning, has achieved impressive developments in recent years. Inspired by its success, this thesis aims to develop new approaches in deep learning to explore and analyze image data from three research themes: classification, retrieval and synthesis. In summary, the research of this thesis contributes at three levels: models and algorithms, practical scenarios and empirical analysis. First, this work presents new approaches based on deep learning to address eight research questions regarding the three themes. In addition, it aims towards adapting the approaches to practical scenarios in real world. Furthermore, this thesis provides numerous experiments and in-depth analysis, which can help motivate further research on the three research themes. Computer Vision Multimedia Applications Deep Learning China Scholarship Council (CSC)Computer Systems, Imagery and Medi

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Humanoid Robots

For many years, the human being has been trying, in all ways, to recreate the complex mechanisms that form the human body. Such task is extremely complicated and the results are not totally satisfactory. However, with increasing technological advances based on theoretical and experimental researches, man gets, in a way, to copy or to imitate some systems of the human body. These researches not only intended to create humanoid robots, great part of them constituting autonomous systems, but also, in some way, to offer a higher knowledge of the systems that form the human body, objectifying possible applications in the technology of rehabilitation of human beings, gathering in a whole studies related not only to Robotics, but also to Biomechanics, Biomimmetics, Cybernetics, among other areas. This book presents a series of researches inspired by this ideal, carried through by various researchers worldwide, looking for to analyze and to discuss diverse subjects related to humanoid robots. The presented contributions explore aspects about robotic hands, learning, language, vision and locomotion