On using gait to enhance frontal face extraction

Visual surveillance finds increasing deployment formonitoring urban environments. Operators need to be able to determine identity from surveillance images and often use face recognition for this purpose. In surveillance environments, it is necessary to handle pose variation of the human head, low frame rate, and low resolution input images. We describe the first use of gait to enable face acquisition and recognition, by analysis of 3-D head motion and gait trajectory, with super-resolution analysis. We use region- and distance-based refinement of head pose estimation. We develop a direct mapping to relate the 2-D image with a 3-D model. In gait trajectory analysis, we model the looming effect so as to obtain the correct face region. Based on head position and the gait trajectory, we can reconstruct high-quality frontal face images which are demonstrated to be suitable for face recognition. The contributions of this research include the construction of a 3-D model for pose estimation from planar imagery and the first use of gait information to enhance the face extraction process allowing for deployment in surveillance scenario

Towards Real-Time Head Pose Estimation: Exploring Parameter-Reduced Residual Networks on In-the-wild Datasets

Head poses are a key component of human bodily communication and thus a decisive element of human-computer interaction. Real-time head pose estimation is crucial in the context of human-robot interaction or driver assistance systems. The most promising approaches for head pose estimation are based on Convolutional Neural Networks (CNNs). However, CNN models are often too complex to achieve real-time performance. To face this challenge, we explore a popular subgroup of CNNs, the Residual Networks (ResNets) and modify them in order to reduce their number of parameters. The ResNets are modifed for different image sizes including low-resolution images and combined with a varying number of layers. They are trained on in-the-wild datasets to ensure real-world applicability. As a result, we demonstrate that the performance of the ResNets can be maintained while reducing the number of parameters. The modified ResNets achieve state-of-the-art accuracy and provide fast inference for real-time applicability.Comment: 32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA/AIE 2019

AN EFFICIENT METHOD TO FEED HIGH RESOLUTION IMAGES TO FACIAL ANALYSIS SYSTEMS

Image Processing is any form of signal processing for which the image is an input such as a photograph or video frame. The output of image processing may be either an image or a set of characteristics or parameters related to the image. In many facial analysis systems like Face Recognition face is used as an important biometric. Facial analysis systems need High Resolution images for their processing. The video obtained from inexpensive surveillance cameras are of poor quality. Processing of poor quality images leads to unexpected results. To detect face images from a video captured by inexpensive surveillance cameras, we will use AdaBoost algorithm. If we feed those detected face images having low resolution and low quality to face recognition systems they will produce some unstable and erroneous results. Because these systems have problem working with low resolution images. Hence we need a method to bridge the gap between on one hand low- resolution and low-quality images and on the other hand facial analysis systems. Our approach is to use a Reconstruction Based Super Resolution method. In Reconstruction Based Super Resolution method we will generate a face-log containing images of similar frontal faces of the highest possible quality using head pose estimation technique. Then, we use a Learning Based Super-Resolution algorithm applied to the result of the reconstruction-based part to improve the quality by another factor of two. Hence the total system quality factor will be improved by four

Wide-range head pose estimation for low resolution video

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, February 2008.Includes bibliographical references (p. 85-87).This thesis focuses on data mining technologies to extract head pose information from low resolution video recordings. Head pose, as an approximation of gaze direction, is a key indicator of human behavior and interaction. Extracting head pose information from video recordings is a labor intensive endeavor that severely limits the feasibility of using large video corpora to perform tasks that require analysis of human behavior. HeadLock is a novel head pose annotation and tracking tool. Pose annotation is formulated as a semiautomatic process in which a human annotator is aided by computationally generated head pose estimates, significantly reducing the human effort required to accurately annotate video recordings. HeadLock has been designed to perform head pose tracking on video from overhead, wide-angle cameras. The head pose estimation system used by HeadLock can perform pose estimation to arbitrary precision on images that reveal only the top or back of a head. This system takes a 3D model-based approach in which heads are modeled as 3D surfaces covered with localized features. The set of features used can be reliably extracted from both hair and skin regions at any resolution, providing better performance for images that may contain small facial regions and no discernible facial features. HeadLock is evaluated on video recorded for the Human Speechome Project (HSP), a research initiative to study human language development by analyzing longitudinal audio-video recordings of a developing child. Results indicate that HeadLock may enable annotation of head pose at ten times the speed of a manual approach. In addition to head tracking, this thesis describes the data collection and data management systems that have been developed for HSP, providing a comprehensive example of how very large corpora of video recordings may be used to research human development, health and behavior.by Philip DeCamp.S.M

3D Gaze Estimation from Remote RGB-D Sensors

The development of systems able to retrieve and characterise the state of humans is important for many applications and fields of study. In particular, as a display of attention and interest, gaze is a fundamental cue in understanding people activities, behaviors, intentions, state of mind and personality. Moreover, gaze plays a major role in the communication process, like for showing attention to the speaker, indicating who is addressed or averting gaze to keep the floor. Therefore, many applications within the fields of human-human, human-robot and human-computer interaction could benefit from gaze sensing. However, despite significant advances during more than three decades of research, current gaze estimation technologies can not address the conditions often required within these fields, such as remote sensing, unconstrained user movements and minimum user calibration. Furthermore, to reduce cost, it is preferable to rely on consumer sensors, but this usually leads to low resolution and low contrast images that current techniques can hardly cope with. In this thesis we investigate the problem of automatic gaze estimation under head pose variations, low resolution sensing and different levels of user calibration, including the uncalibrated case. We propose to build a non-intrusive gaze estimation system based on remote consumer RGB-D sensors. In this context, we propose algorithmic solutions which overcome many of the limitations of previous systems. We thus address the main aspects of this problem: 3D head pose tracking, 3D gaze estimation, and gaze based application modeling. First, we develop an accurate model-based 3D head pose tracking system which adapts to the participant without requiring explicit actions. Second, to achieve a head pose invariant gaze estimation, we propose a method to correct the eye image appearance variations due to head pose. We then investigate on two different methodologies to infer the 3D gaze direction. The first one builds upon machine learning regression techniques. In this context, we propose strategies to improve their generalization, in particular, to handle different people. The second methodology is a new paradigm we propose and call geometric generative gaze estimation. This novel approach combines the benefits of geometric eye modeling (normally restricted to high resolution images due to the difficulty of feature extraction) with a stochastic segmentation process (adapted to low-resolution) within a Bayesian model allowing the decoupling of user specific geometry and session specific appearance parameters, along with the introduction of priors, which are appropriate for adaptation relying on small amounts of data. The aforementioned gaze estimation methods are validated through extensive experiments in a comprehensive database which we collected and made publicly available. Finally, we study the problem of automatic gaze coding in natural dyadic and group human interactions. The system builds upon the thesis contributions to handle unconstrained head movements and the lack of user calibration. It further exploits the 3D tracking of participants and their gaze to conduct a 3D geometric analysis within a multi-camera setup. Experiments on real and natural interactions demonstrate the system is highly accuracy. Overall, the methods developed in this dissertation are suitable for many applications, involving large diversity in terms of setup configuration, user calibration and mobility

Audio head pose estimation using the direct to reverberant speech ratio

Head pose is an important cue in many applications such as, speech recognition and face recognition. Most approaches to head pose estimation to date have focussed on the use of visual information of a subject’s head. These visual approaches have a number of limitations such as, an inability to cope with occlusions, changes in the appearance of the head, and low resolution images. We present here a novel method for determining coarse head pose orientation purely from audio information, exploiting the direct to reverberant speech energy ratio (DRR) within a reverberant room environment. Our hypothesis is that a speaker facing towards a microphone will have a higher DRR and a speaker facing away from the microphone will have a lower DRR. This method has the advantage of actually exploiting the reverberations within a room rather than trying to suppress them. This also has the practical advantage that most enclosed living spaces, such as meeting rooms or offices are highly reverberant environments. In order to test this hypothesis we also present a new data set featuring 56 subjects recorded in three different rooms, with different acoustic properties, adopting 8 different head poses in 4 different room positions captured with a 16 element microphone array. As far as the authors are aware this data set is unique and will make a significant contribution to further work in the area of audio head pose estimation. Using this data set we demonstrate that our proposed method of using the DRR for audio head pose estimation provides a significant improvement over previous methods

Unobtrusive and pervasive video-based eye-gaze tracking

Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe