Zeynep Yücel

Robustifying eye center localization by head pose cues

Head pose and eye location estimation are two closely related issues which refer to similar application areas. In recent years, these problems have been studied individually in numerous works in the literature. Previous research shows that cylindrical head models and isophote based schemes provide satisfactory precision in head pose and eye location estimation, respectively. However, the eye locator is not adequate to accurately locate eye in the presence of extreme head poses. Therefore, head pose cues may be suited to enhance the accuracy of eye localization in the presence of severe head poses. In this paper, a hybrid scheme is proposed in which the transformation matrix obtained from the head pose is used to normalize the eye regions and, in turn the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to (1) enhance the accuracy of eye location estimations in low resolution videos, (2) to extend the operating range of the eye locator and (3) to improve the accuracy and re-initialization capabilities of the pose tracker. From the experimental results it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Further, it considerably extends its operating range by more than 15°, by overcoming the problems introduced by extreme head poses. Finally, the accuracy of the head pose tracker is improved by 12% to 24%.

Joint Attention by Gaze Interpolation and Saliency

Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenters eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention.

Joint visual attention modeling for naturally interacting robotic agents

This paper elaborates on mechanisms for establishing visual joint attention for the design of robotic agents that learn through natural interfaces, following a developmental trajectory not unlike infants. We describe first the evolution of cognitive skills in infants and then the adaptation of cognitive development patterns in robotic design. A comprehensive outlook for cognitively inspired robotic design schemes pertaining to joint attention is presented for the last decade, with particular emphasis on practical implementation issues. A novel cognitively inspired joint attention fixation mechanism is defined for robotic agents.

Resolution of focus of attention using gaze direction estimation and saliency computation

Modeling the users attention is useful for responsive and interactive systems. This paper proposes a method for establishing joint visual attention between an experimenter and an intelligent agent. A rapid procedure is described to track the 3D head pose of the experimenter, which is used to approximate the gaze direction. The head is modeled with a sparse grid of points sampled from the surface of a cylinder. We then propose to employ a bottom-up saliency model to single out interesting objects in the neighborhood of the estimated focus of attention. We report results on a series of experiments, where a human experimenter looks at objects placed at different locations of the visual field, and the proposed algorithm is used to locate target objects automatically. Our results indicate that the proposed approach achieves high localization accuracy and thus constitutes a useful tool for the construction of natural human-computer interfaces.

Watermarking via zero assigned filter banks

In order to identify the owner and distributor of digital data, a watermarking scheme in frequency domain for multimedia files is proposed. The scheme satisfies the imperceptibility and persistence requirements and it is robust against additive noise. It consists of a few stages of wavelet decomposition of several subblocks of the original signal using special zero assigned filter banks. By assigning zeros to filters on the high frequency portion of the spectrum, filter banks with frequency selective response are obtained. The information is then inserted in the wavelet-decomposed and compressed signal. Several robustness tests are performed on male voice, female voice, and music files, color and gray level images. The algorithm is tested under white Gaussian noise and against JPEG compression and it is observed to be robust even when exposed to high levels of corruption.

Automated discrimination of psychotropic drugs in mice via computer vision-based analysis

We developed an inexpensive computer vision-based method utilizing an algorithm which differentiates drug-induced behavioral alterations. The mice were observed in an open-field arena and their activity was recorded for 100 min. For each animal the first 50 min of observation were regarded as the drug-free period. Each animal was exposed to only one drug and they were injected (i.p.) with either amphetamine or cocaine as the stimulant drugs or morphine or diazepam as the inhibitory agents. The software divided the arena into virtual grids and calculated the number of visits (sojourn counts) to the grids and instantaneous speeds within these grids by analyzing video data. These spatial distributions of sojourn counts and instantaneous speeds were used to construct feature vectors which were fed to the classifier algorithms for the final step of matching the animals and the drugs. The software decided which of the animals were drug-treated at a rate of 96%. The algorithm achieved 92% accuracy in sorting the data according to the increased or decreased activity and then determined which drug was delivered. The method differentiated the type of psychostimulant or inhibitory drugs with a success ratio of 70% and 80%, respectively. This method provides a new way to automatically evaluate and classify drug-induced behaviors in mice.

Derivation of gaze direction from head pose estimates

Automatic estimation of gaze direction information is important for certain applications of human-robot and human-computer interaction. Depending on the properties of the specific application, it may be required to derive this information in real time from low resolution visual inputs, with as much precision as possible. In this paper we present an algorithm for transforming head pose estimates to gaze direction estimates. The main contribution of this study lies in the fact that it makes a clear distinction between head pose and gaze direction. Unlike some of the previous works in this field, we do not correct the head pose to correspond to a possible attention fixation point in accordance with the experiment scenario. Instead we propose using a concrete and environment-independent method for this purpose. To transform the head pose estimates into gaze direction, a Gaussian process regression model is proposed and the reasons validating this choice are discussed in detail.

Detection of epilepsy seizures and epileptic indicators in EEG signals

Symptoms of epilepsy, which is characterized by abnormal brain electrical activity, can be observed on electroencephalography (EEG) signal. This paper employs models of chaotic measures of EEG and aims to help detection of epilepsy seizures and diagnosis of epileptic indicators in seizure-free signals.