Recognition of human head orientation based on artificial neural networks

Rae, Robert; Helge, J. Ritter

doi:10.1016/s0920-5489(99)90850-4

Cited by 17 publications

(23 citation statements)

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“…al. [6]. As compared to their results, we did not observe serious degradation on data from new users.…”

Section: Training and Resultssupporting

confidence: 53%

“…al. [6] describe a user dependent neural network based system to estimate pan and tilt of a person. In their approach, color segmentation, ellipse fitting and Gabor-filtering on a segmented face are used for preprocessing.…”

Section: Estimating Head Pose With Neural Netsmentioning

confidence: 99%

“…Detecting the facial features, however, is a challenging problem and tracking is likely to fail. Example based approaches either use some kind of function approximation technique such as neural networks [1,7,6], or a face database [4] to encode example images. Head pose of new images is then estimated using the function approximator, such as the neural networks, or by matching novel images to the examples in the database.…”

Section: Introductionmentioning

confidence: 99%

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From Gaze to Focus of Attention

Stiefelhagen¹,

Finke

Yang

et al. 1999

Visual Information and Information Systems

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“…al. [6]. As compared to their results, we did not observe serious degradation on data from new users.…”

Section: Training and Resultssupporting

confidence: 53%

Section: Estimating Head Pose With Neural Netsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

From Gaze to Focus of Attention

Stiefelhagen¹,

Finke

Yang

et al. 1999

Visual Information and Information Systems

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show abstract

“…Model-based approaches typically recover the face pose by establishing the relationship between 3D face model and its twodimensional (2D) projection [26][27][28]. Appearance-based approaches are based on view interpolation and their goal is to construct an association between appearance and face orientation [29][30][31]. Although appearancebased methods are simpler, they are expected to be less accurate than model-based approaches and are mainly used for pose discrimination.…”

Section: Face (Head) Orientation Estimationmentioning

confidence: 99%

“…Of the numerous techniques proposed for gaze estimation [31][32][33], the one proposed by Ebisawa [34] appears very promising and is directly applicable to this project. Their technique estimates the gaze direction based on the relative position between pupil and the glint.…”

Section: Eye-gaze Determination and Trackingmentioning

confidence: 99%

Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance

2002

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T his paper describes a real-time prototype computer vision system for monitoring driver vigilance. The main components of the system consists of a remotely located video CCD camera, a specially designed hardware system for real-time image acquisition and for controlling the illuminator and the alarm system, and various computer vision algorithms for simultaneously, real-time and non-intrusively monitoring various visual bio-behaviors that typically characterize a driver's level of vigilance. The visual behaviors include eyelid movement, face orientation, and gaze movement (pupil movement). The system was tested in a simulating environment with subjects of different ethnic backgrounds, different genders, ages, with/without glasses, and under different illumination conditions, and it was found very robust, reliable and accurate. IntroductionThe ever-increasing number of traffic accidents in the US due to a diminished driver's vigilance level has become a problem of serious concern to society. Drivers with a diminished vigilance level suffer from a marked decline in their abilities of perception, recognition, and vehicle control, and therefore pose serious danger to their own life and the lives of other people. Statistics show that a leading cause for fatal or injury-causing traffic accidents is due to drivers with a diminished vigilance level. In the trucking industry, 57% fatal truck accidents are due to driver fatigue. It is the number 1 cause for heavy truck crashes. Seventy percent of American drivers report driving fatigued. With the ever-growing traffic conditions, this problem will further deteriorate. For this reason, developing systems actively monitoring a driver's level of vigilance and alerting the driver of any insecure driving conditions is essential to prevent accidents.Many efforts [1][2][3][4][5][6][7][8][9] have been reported in the literature for developing active safety systems intended for reducing the number of automobile accidents due to Real-Time Imaging 8, 357-377 (2002Imaging 8, 357-377 ( ) doi:10.1006Imaging 8, 357-377 ( /rtim.2002, available online at http://www.idealibrary.com on reduced vigilance. Among different techniques, the best detection accuracy is achieved with techniques that measure physiological conditions like brain waves, heart rate, and pulse rate [8,10] People in fatigue exhibit certain visual behaviors easily observable from changes in their facial features like the eyes, head, and face. Typical visual characteristics observable from the image of a person with reduced alertness level include slow eyelid movement [12,13], smaller degree of eye openness (or even closed), frequent nodding [14], yawning, gaze (narrowness in the line of sight), sluggish in facial expression, and sagging posture. To make use of these visual cues, another increasingly popular and non-invasive approach for monitoring fatigue is to assess a driver's vigilance level through visual observation of his/her physical conditions using a camera and state-of-the-art technologies in computer vision. ...

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Perception and Interactive Technologies

2006

Lecture Notes in Computer Science

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This paper briefly summarises our results on gaze guidance such as to complement the demonstrations that we plan to present at the workshop. Our goal is to integrate gaze into visual communication systems by measuring and guiding eye movements. Our strategy is to predict a set of about ten salient locations and then change the probability for one of these candidates to be attended: for one candidate the probability is increased, for the others it is decreased. To increase saliency, in our current implementation, we show a natural-scene movie and overlay red dots very briefly such that they are hardly perceived consciously. To decrease the probability, for example, we locally reduce the temporal frequency content of the movie. We here present preliminary results, which show that the three steps of our above strategy are feasible. The long-term goal is to find the optimal real-time video transformation that minimises the difference between the actual and the desired eye movements without being obtrusive. Applications are in the area of vision-based communication, augmented vision, and learning.Abstract. In this contribution we extend existing methods for head pose estimation and investigate the use of local image phase for gaze detection. Moreover we describe how a small database of face images with given ground truth for head pose and gaze direction was acquired. With this database we compare two different computational approaches for extracting the head pose. We demonstrate that a simple implementation of the proposed methods without extensive training sessions or calibration is sufficient to accurately detect the head pose for human-computer interaction. Furthermore, we propose how eye gaze can be extracted based on the outcome of local filter responses and the detected head pose. In all, we present a framework where different approaches are combined to a single system for extracting information about the attentional state of a person. Abstract.A behavioral nonlinear dynamics model of multistable perception due to ambiguous visual stimuli is presented. The perception state is formalized as the dynamic phase variable v(t) of a recursive process with cosinuidal transfer characteristic which is created by superposition (interference) of neuronal mean fields. The two parameters μ = difference of meaning of alternative percepts and G = attention parameter, control the transition between unambiguous and ambiguous stimuli, e.g. from stimulus off to stimulus on, and attention fatigue respectively. Mean field interference with delayed phase feedback enables transitions between chaotic and limit cycle attractors v(t) representing the perception states. Perceptual reversals are induced by attention fatigue G(t) (~ adaptive gain g(v)) with time constant γ, and attention bias which determines the relative duration of the percepts. The coupled attention -perception dynamics with an additive stochastic noise term reproduces the experimentally observed Γ-distribution of the reversal time statistics. Mean reversal times of typic...

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Recognition of human head orientation based on artificial neural networks

Cited by 17 publications

References 0 publications

From Gaze to Focus of Attention

From Gaze to Focus of Attention

Real-Time Eye, Gaze, and Face Pose Tracking for Monitoring Driver Vigilance

Perception and Interactive Technologies

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