Estimating Point of Regard with a Consumer Camera at a Distance

In this work, we have studied the viability of a novel technique to estimate the POR that only requires video feed from a consumer camera. The system can work under uncontrolled light conditions and does not require any complex hardware setup. To that end we propose a system that uses PCA feature extraction from the eyes region followed by non-linear regression. We evaluated three state of the art non-linear regression algorithms. In the study, we also compared the performance using a high quality webcam versus a Kinect sensor. We found, that despite the relatively low quality of the Kinect images it achieves similar performance compared to the high quality camera. These results show that the proposed approach could be extended to estimate POR in a completely non-intrusive way.Mansanet Sandin, J.; Albiol Colomer, A.; Paredes Palacios, R.; Mossi García, JM.; Albiol Colomer, AJ. (2013). Estimating Point of Regard with a Consumer Camera at a Distance. En Pattern Recognition and Image Analysis. Springer Verlag. 7887:881-888. doi:10.1007/978-3-642-38628-2_104S8818887887Baluja, S., Pomerleau, D.: Non-intrusive gaze tracking using artificial neural networks. Technical report (1994)Breiman, L.: Random forests. Machine Learning (2001)Logitech HD Webcam C525, http://www.logitech.com/es-es/webcam-communications/webcams/hd-webcam-c525Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM TIST (2011), Software, http://www.csie.ntu.edu.tw/~cjlin/libsvmDrucker, H., Burges, C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines (1996)Hansen, D.W., Ji, Q. In: the eye of the beholder: A survey of models for eyes and gaze. IEEE Transactions on PAMI (2010)Ji, Q., Yang, X.: Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging (2002)Kalman, R.E.: A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering (1960)Microsoft Kinect, http://www.microsoft.com/en-us/kinectforwindowsTimmerman, M.E.: Principal component analysis (2nd ed.). i. t. jolliffe. Journal of the American Statistical Association (2003)Morimoto, C.H., Mimica, M.R.M.: Eye gaze tracking techniques for interactive applications. Comput. Vis. Image Underst. (2005)Pirri, F., Pizzoli, M., Rudi, A.: A general method for the point of regard estimation in 3d space. In: Proceedings of the IEEE Conference on CVPR (2011)Reale, M.J., Canavan, S., Yin, L., Hu, K., Hung, T.: A multi-gesture interaction system using a 3-d iris disk model for gaze estimation and an active appearance model for 3-d hand pointing. IEEE Transactions on Multimedia (2011)Saragih, J.M., Lucey, S., Cohn, J.F.: Face alignment through subspace constrained mean-shifts. In: International Conference of Computer Vision, ICCV (2009)Kar-Han, T., Kriegman, D.J., Ahuja, N.: Appearance-based eye gaze estimation. In: Applications of Computer Vision (2002)Takemura, K., Kohashi, Y., Suenaga, T., Takamatsu, J., Ogasawara, T.: Estimating 3d point-of-regard and visualizing gaze trajectories under natural head movements. In: Symposium on Eye-Tracking Research and Applications (2010)Villanueva, A., Cabeza, R., Porta, S.: Eye tracking: Pupil orientation geometrical modeling. Image and Vision Computing (2006)Williams, O., Blake, A., Cipolla, R.: Sparse and semi-supervised visual mapping with the s3gp. In: IEEE Computer Society Conference on CVPR (2006

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

3D pointing gesture recognition for human-robot interaction

In this paper, a pointing gesture recognition method is proposed for human-robot interaction. The pointing direction of the human partner is obtained by extracting the joint coordinates and computing through vector calculations. 3D to 2D mapping is implemented to build a top-view 2D map with respect to the actual ground circumstance. Using this method, robot is able to interpret the human partner’s 3D pointing gesture based on the coordinate information of his/her shoulder and hand. Besides this, speed control of robot can be achieved by adjusting the position of the human partner’s hand relative to the head. The recognition performance and viability of the system are tested through quantitative experiments

Face pose estimation in monocular images

People use orientation of their faces to convey rich, inter-personal information. For example, a person will direct his face to indicate who the intended target of the conversation is. Similarly in a conversation, face orientation is a non-verbal cue to listener when to switch role and start speaking, and a nod indicates that a person has understands, or agrees with, what is being said. Further more, face pose estimation plays an important role in human-computer interaction, virtual reality applications, human behaviour analysis, pose-independent face recognition, driver s vigilance assessment, gaze estimation, etc. Robust face recognition has been a focus of research in computer vision community for more than two decades. Although substantial research has been done and numerous methods have been proposed for face recognition, there remain challenges in this field. One of these is face recognition under varying poses and that is why face pose estimation is still an important research area. In computer vision, face pose estimation is the process of inferring the face orientation from digital imagery. It requires a serious of image processing steps to transform a pixel-based representation of a human face into a high-level concept of direction. An ideal face pose estimator should be invariant to a variety of image-changing factors such as camera distortion, lighting condition, skin colour, projective geometry, facial hairs, facial expressions, presence of accessories like glasses and hats, etc. Face pose estimation has been a focus of research for about two decades and numerous research contributions have been presented in this field. Face pose estimation techniques in literature have still some shortcomings and limitations in terms of accuracy, applicability to monocular images, being autonomous, identity and lighting variations, image resolution variations, range of face motion, computational expense, presence of facial hairs, presence of accessories like glasses and hats, etc. These shortcomings of existing face pose estimation techniques motivated the research work presented in this thesis. The main focus of this research is to design and develop novel face pose estimation algorithms that improve automatic face pose estimation in terms of processing time, computational expense, and invariance to different conditions

Eyewear Computing \u2013 Augmenting the Human with Head-Mounted Wearable Assistants

The seminar was composed of workshops and tutorials on head-mounted eye tracking, egocentric vision, optics, and head-mounted displays. The seminar welcomed 30 academic and industry researchers from Europe, the US, and Asia with a diverse background, including wearable and ubiquitous computing, computer vision, developmental psychology, optics, and human-computer interaction. In contrast to several previous Dagstuhl seminars, we used an ignite talk format to reduce the time of talks to one half-day and to leave the rest of the week for hands-on sessions, group work, general discussions, and socialising. The key results of this seminar are 1) the identification of key research challenges and summaries of breakout groups on multimodal eyewear computing, egocentric vision, security and privacy issues, skill augmentation and task guidance, eyewear computing for gaming, as well as prototyping of VR applications, 2) a list of datasets and research tools for eyewear computing, 3) three small-scale datasets recorded during the seminar, 4) an article in ACM Interactions entitled \u201cEyewear Computers for Human-Computer Interaction\u201d, as well as 5) two follow-up workshops on \u201cEgocentric Perception, Interaction, and Computing\u201d at the European Conference on Computer Vision (ECCV) as well as \u201cEyewear Computing\u201d at the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)

Eye tracking and gaze interface design for pervasive displays

Eye tracking for pervasive displays in everyday computing is an emerging area in research. There is an increasing number of pervasive displays in our surroundings, such as large displays in public spaces, digital boards in offices and smart televisions at home. Gaze is an attractive input modality for these displays, as people naturally look at objects of interest and use their eyes to seek information. Existing research has applied eye tracking in a variety of fields, but tends to be in constrained environments for lab applications. This thesis investigates how to enable robust gaze sensing in pervasive contexts and how eye tracking can be applied for pervasive displays that we encounter in our daily life. To answer these questions, we identify the technical and design challenges posed by using gaze for pervasive displays. Firstly, in out-of-lab environments, interactions are usually spontaneous where users and systems are unaware of each other beforehand. This poses the technical problem that gaze sensing should not need prior user training and should be robust in unconstrained environments. We develop novel vision-based systems that require only off-the-shelf RGB cameras to address this issue. Secondly, in pervasive contexts, users are usually unaware of gaze interactivity iii of pervasive displays and the technical restrictions of gaze sensing systems. However, there is little knowledge about how to enable people to use gaze interactive systems in daily life. Thus, we design novel interfaces that allow novice users to interact with contents on pervasive displays, and we study the usage of our systems through field deployments. We demonstrate that people can walk up to a gaze interactive system and start to use it immediately without human assistance. Lastly, pervasive displays could also support multiuser co-located collaborations. We explore the use of gaze for collaborative tasks. Our results show that sharing gaze information on shared displays can ease communications and improve collaboration. Although we demonstrate benefits of using gaze for pervasive displays, open challenges remain in enabling gaze interaction in everyday computing and require further investigations. Our research provides a foundation for the rapidly growing field of eye tracking for pervasive displays