Eye tracking using markov models

We propose an eye detection and tracking method based on color and geometrical features of the human face using a monocular camera. In this method a decision is made on whether the eyes are closed or not and, using a Markov chain framework to model temporal evolution, the subject's gaze is determined. The method can successfully track facial features even while the head assumes various poses, so long as the nostrils are visible to the camera. We compare our method with recently proposed techniques and results show that it provides more accurate tracking and robustness to variations in view of the face. A procedure for detecting tracking errors is employed to recover the loss of feature points in case of occlusion or very fast head movement. The method may be used in monitoring a driver's alertness and detecting drowsiness, and also in applications requiring non-contact human computer interaction

A framework for context-aware driver status assessment systems

The automotive industry is actively supporting research and innovation to meet manufacturers' requirements related to safety issues, performance and environment. The Green ITS project is among the efforts in that regard. Safety is a major customer and manufacturer concern. Therefore, much effort have been directed to developing cutting-edge technologies able to assess driver status in term of alertness and suitability. In that regard, we aim to create with this thesis a framework for a context-aware driver status assessment system. Context-aware means that the machine uses background information about the driver and environmental conditions to better ascertain and understand driver status. The system also relies on multiple sensors, mainly video and audio. Using context and multi-sensor data, we need to perform multi-modal analysis and data fusion in order to infer as much knowledge as possible about the driver. Last, the project is to be continued by other students, so the system should be modular and well-documented. With this in mind, a driving simulator integrating multiple sensors was built. This simulator is a starting point for experimentation related to driver status assessment, and a prototype of software for real-time driver status assessment is integrated to the platform. To make the system context-aware, we designed a driver identification module based on audio-visual data fusion. Thus, at the beginning of driving sessions, the users are identified and background knowledge about them is loaded to better understand and analyze their behavior. A driver status assessment system was then constructed based on two different modules. The first one is for driver fatigue detection, based on an infrared camera. Fatigue is inferred via percentage of eye closure, which is the best indicator of fatigue for vision systems. The second one is a driver distraction recognition system, based on a Kinect sensor. Using body, head, and facial expressions, a fusion strategy is employed to deduce the type of distraction a driver is subject to. Of course, fatigue and distraction are only a fraction of all possible drivers' states, but these two aspects have been studied here primarily because of their dramatic impact on traffic safety. Through experimental results, we show that our system is efficient for driver identification and driver inattention detection tasks. Nevertheless, it is also very modular and could be further complemented by driver status analysis, context or additional sensor acquisition

Guidance in the human–machine analytics process

In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making

Theory of mind and information relevance in human centric human robot cooperation

In the interaction with others, besides consideration of environment and task requirements, it is crucial to account for and develop an understanding for the interaction partner and her state of mind. An understanding of other’s state of knowledge and plans is important to support efficient interaction activities including information sharing, or distribution of sub- tasks. A robot cooperating with and supporting a human partner might decide to communicate information that it has collected. However, sharing every piece of information is not feasible, as not all information is both, currently relevant and new for the human partner, but instead will annoy and dis- tract her from other important activities. An understanding for the human state of mind will enable the robot to balance communication according to the needs of the human partner and the efforts of communication for both. An artificial theory of mind is proposed as Bayesian inference of human beliefs during interaction. It relies on a general model for human information perception and decision making. To cope with the complexity of second order inference – estimating what the human inferred of her environment – an efficient linearization based filtering approach is introduced. The inferred human belief, as understanding of her mental state, is used to estimate her situation awareness. When this is missing, e.g. the human is unaware of some important piece of information, the robot provides supportive communication. It therefore evaluates relevance and novelty of information compared to communication efforts following a systematic information sharing concept. The robot decides about whether, when and what type of information it should provide in a current situation to sup- port the human efficiently without annoying. The decision is derived by planning under uncertainty while considering the inferred human belief in relation to the task requirements. Systematic properties and benefits of the derived concepts are discussed in illustrative example situations. Two human robot collaborative tasks and corresponding user studies were designed and investigated, applying artificial theory of mind as be- lief inference and assistive communication in the interaction with humans. Equipped with the artificial theory of mind, the robot is able to infer in- terpretable information about the human’s mental state and can detect a lack of human awareness. Supported by adaptive human centric information sharing, participants could recover much earlier from unawareness. A comparison to state-of-the-art communication strategies demonstrates the efficiency, as the new concept explicitly balances benefits and costs of communication to support while avoiding unnecessary interruptions. By sharing information according to human needs and environmental urgency, the robot does not take over nor instruct the human, but enables her to make good decisions herself

Eye Tracking: A Perceptual Interface for Content Based Image Retrieval

In this thesis visual search experiments are devised to explore the feasibility of an eye gaze driven search mechanism. The thesis first explores gaze behaviour on images possessing different levels of saliency. Eye behaviour was predominantly attracted by salient locations, but appears to also require frequent reference to non-salient background regions which indicated that information from scan paths might prove useful for image search. The thesis then specifically investigates the benefits of eye tracking as an image retrieval interface in terms of speed relative to selection by mouse, and in terms of the efficiency of eye tracking mechanisms in the task of retrieving target images. Results are analysed using ANOVA and significant findings are discussed. Results show that eye selection was faster than a computer mouse and experience gained during visual tasks carried out using a mouse would benefit users if they were subsequently transferred to an eye tracking system. Results on the image retrieval experiments show that users are able to navigate to a target image within a database confirming the feasibility of an eye gaze driven search mechanism. Additional histogram analysis of the fixations, saccades and pupil diameters in the human eye movement data revealed a new method of extracting intentions from gaze behaviour for image search, of which the user was not aware and promises even quicker search performances. The research has two implications for Content Based Image Retrieval: (i) improvements in query formulation for visual search and (ii) new methods for visual search using attentional weighting. Futhermore it was demonstrated that users are able to find target images at sufficient speeds indicating that pre-attentive activity is playing a role in visual search. A current review of eye tracking technology, current applications, visual perception research, and models of visual attention is discussed. A review of the potential of the technology for commercial exploitation is also presented

Study of pupil diameter and eye movements to enhance flight safety. Etude de diamètre pupillaire et de mouvements oculaires pour la sécurité aérienne

L'analyse d'événements aériens révèle que la plupart des accidents aéronautiques ont pour origine une surveillance inadaptée de paramètres de vol induite par une vigilance réduite, le stress ou une charge de travail importante. Une solution prometteuse pour améliorer la sécurité aérienne est d'étudier le regard des pilotes. La pupille est un bon indicateur de l'état attentionnel/cognitif tandis que les mouvements oculaires révèlent des stratégies de prises d'information. La question posée dans ce manuscrit est d'évaluer l'apport de l'oculométrie pour la sécurité aérienne par les contributions suivantes : 1-2) Les deux premières études de ce doctorat ont démontré que les effets d'interaction entre la luminance et la charge cognitive sur la réaction pupillaire. La composante pupillaire impactée dépend de la nature de la charge - soutenue ou transitoire. 3) Un cadre mathématique développé fournit un moyen d'illustration de schémas visuels pour l'analyse qualitative. Ce cadre ouvre également la voie à de nouvelles méthodes pour comparer quantitativement ces schémas visuels. 4) Une technique originale d'analyse de fixations et de construction d'un ratio "exploration-exploitation" est proposée et est appliquée dans deux cas d'études en simulateur de vol. 5) Enfin, on propose un cadre théorique d'intégration de l'oculométrie dans les cockpits. Ce cadre comporte quatre étapes présentées dans, à la fois, l'ordre chronologique de l'intégration et la complexité technique de réalisation.Most aviation accidents include failures in monitoring or decision-making which are hampered by arousal, stress or high workload. One promising avenue to further enhance the flight safety is looking into the pilots' eyes. The pupil is a good indicator of cognitive/attentional states while eye movements reveal monitoring strategies. This thesis reflected upon the application of eye tracking in aviation with following contributions: 1-2) The two pupil experiments revealed that the luminance impacts the cognitive pupil reaction. Depending on the nature of the cognitive load - sustained or transient - the corresponding pupillary component would be impacted. The same amount of cognitive load under dimmer luminance condition would elicit larger tonic pupil diameter in a sustained load paradigm and larger phasic pupil response in a transient load paradigm. 3) We designed a novel mathematical framework and method that provide comprehensive illustrations of scanpaths for qualitative analysis. This framework also makes a lane for new methods of scanpaths comparison. 4) The developed technique of analysis of fixations and construction of "explore-exploit" ratio is presented and verifed on the data from two experiments in flight simulators. 5) Eventually, we proposed a framework of eye tracking integration into the cockpits. It contains four stages presented in both chronological order of its integration and technical complexity

Development and Evaluation of Facial Gesture Recognition and Head Tracking for Assistive Technologies

Globally, the World Health Organisation estimates that there are about 1 billion people suffering from disabilities and the UK has about 10 million people suffering from neurological disabilities in particular. In extreme cases these individuals with disabilities such as Motor Neuron Disease(MND), Cerebral Palsy(CP) and Multiple Sclerosis(MS) may only be able to perform limited head movement, move their eyes or make facial gestures. The aim of this research is to investigate low-cost and reliable assistive devices using automatic gesture recognition systems that will enable the most severely disabled user to access electronic assistive technologies and communication devices thus enabling them to communicate with friends and relative. The research presented in this thesis is concerned with the detection of head movements, eye movements, and facial gestures, through the analysis of video and depth images. The proposed system, using web cameras or a RGB-D sensor coupled with computer vision and pattern recognition techniques, will have to be able to detect the movement of the user and calibrate it to facilitate communication. The system will also provide the user with the functionality of choosing the sensor to be used i.e. the web camera or the RGB-D sensor, and the interaction or switching mechanism i.e. eye blink or eyebrows movement to use. This ability to system to enable the user to select according to the user's needs would make it easier on the users as they would not have to learn how to operating the same system as their condition changes. This research aims to explore in particular the use of depth data for head movement based assistive devices and the usability of different gesture modalities as switching mechanisms. The proposed framework consists of a facial feature detection module, a head tracking module and a gesture recognition module. Techniques such as Haar-Cascade and skin detection were used to detect facial features such as the face, eyes and nose. The depth data from the RGB-D sensor was used to segment the area nearest to the sensor. Both the head tracking module and the gesture recognition module rely on the facial feature module as it provided data such as the location of the facial features. The head tracking module uses the facial feature data to calculate the centroid of the face, the distance to the sensor, the location of the eyes and the nose to detect head motion and translate it into pointer movement. The gesture detection module uses features such as the location of the eyes, the location of the pupil, the size of the pupil and calculates the interocular distance for the detection of blink or eyebrows movement to perform a click action. The research resulted in the creation of four assistive devices based on the combination of the sensors (Web Camera and RGB-D sensor) and facial gestures (Blink and Eyebrows movement): Webcam-Blink, Webcam-Eyebrows, Kinect-Blink and Kinect-Eyebrows. Another outcome of this research has been the creation of an evaluation framework based on Fitts' Law with a modified multi-directional task including a central location and a dataset consisting of both colour images and depth data of people performing head movement towards different direction and performing gestures such as eye blink, eyebrows movement and mouth movements. The devices have been tested with healthy participants. From the observed data, it was found that both Kinect-based devices have lower Movement Time and higher Index of Performance and Effective Throughput than the web camera-based devices thus showing that the introduction of the depth data has had a positive impact on the head tracking algorithm. The usability assessment survey, suggests that there is a significant difference in eye fatigue experienced by the participants; blink gesture was less tiring to the eye than eyebrows movement gesture. Also, the analysis of the gestures showed that the Index of Difficulty has a large effect on the error rates of the gesture detection and also that the smaller the Index of Difficulty the higher the error rate