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Fadi Dornaika, & Angel Sappa. (2008). Real Time on Board Stereo Camera Pose through Image Registration. In IEEE Intelligent Vehicles Symposium, (804–809).
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Fadi Dornaika, & Angel Sappa. (2008). Evaluation of an Appearance-based 3D Face Tracker using Dense 3D Data. Machine Vision and Applications, 427–441.
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Fadi Dornaika, & Angel Sappa. (2007). Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data. In H. Araujo and J. Jorge A. R. J. Braz (Ed.), Advances in Computer Graphics and Computer Vision, (354–366). Springer Verlag.
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Fadi Dornaika, & Angel Sappa. (2008). Real Time Image Registration for Planar Structure and 3D Sensor Pose Estimation. In Asim Bhatti (Ed.), Stereo Vision (Vol. 18, 299–316).
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Fadi Dornaika, & Angel Sappa. (2009). Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression. PRL - Pattern Recognition Letters, 30(5), 535–543.
Abstract: This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes.
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Fadi Dornaika, & Angel Sappa. (2009). A Featureless and Stochastic Approach to On-board Stereo Vision System Pose. IMAVIS - Image and Vision Computing, 27(9), 1382–1393.
Abstract: This paper presents a direct and stochastic technique for real-time estimation of on-board stereo head’s position and orientation. Unlike existing works which rely on feature extraction either in the image domain or in 3D space, our proposed approach directly estimates the unknown parameters from the stream of stereo pairs’ brightness. The pose parameters are tracked using the particle filtering framework which implicitly enforces the smoothness constraints on the estimated parameters. The proposed technique can be used with a driver assistance applications as well as with augmented reality applications. Extended experiments on urban environments with different road geometries are presented. Comparisons with a 3D data-based approach are presented. Moreover, we provide a performance study aiming at evaluating the accuracy of the proposed approach.
Keywords: On-board stereo vision system; Pose estimation; Featureless approach; Particle filtering; Image warping
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Fadi Dornaika, & Bogdan Raducanu. (2007). Efficient Facial Expression Recognition for Human Robot Interaction. In Computational and Ambient Intelligence, 9th International Work–Conference on Artificial Neural Networks (Vol. 4507, 700–708). LNCS.
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Fadi Dornaika, & Bogdan Raducanu. (2006). Recognizing Facial Expressions in Videos Using a Facial Action Analysis-Synthesis Scheme.
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Fadi Dornaika, & Bogdan Raducanu. (2007). Inferring Facial Expressions from Videos: Tool and Application. Signal Processing: Image Communication, vol. 22(9):769–784.
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Fadi Dornaika, & Bogdan Raducanu. (2008). Detecting and Tracking of 3D Face Pose for Human-Robot Interaction. In IEEE International Conference on Robotics and Automation, (1716–1721).
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Fadi Dornaika, & Bogdan Raducanu. (2008). Constructing Panoramic Views Through Facial Gaze Tracking. In IEEE International Conference on Multimedia and Expo, (969–972).
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Fadi Dornaika, & Bogdan Raducanu. (2008). Facial Expression Recognition for HCI Applications. In Rabuñal (Ed.), Encyclopedia of Artificial Intelligence (Vol. II, 625–631). IGI–Global Publisher.
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Fadi Dornaika, & Bogdan Raducanu. (2008). 3D Face Pose Detection and Tracking Using Monocular Videos: Tool and Application. IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE).
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Fadi Dornaika, & Bogdan Raducanu. (2009). Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application. TSMCB - IEEE Transactions on Systems, Man and Cybernetics part B, 39(4), 935–944.
Abstract: Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods.
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Fadi Dornaika, & Bogdan Raducanu. (2009). Simultaneous 3D face pose and person-specific shape estimation from a single image using a holistic approach. In IEEE Workshop on Applications of Computer Vision.
Abstract: This paper presents a new approach for the simultaneous estimation of the 3D pose and specific shape of a previously unseen face from a single image. The face pose is not limited to a frontal view. We describe a holistic approach based on a deformable 3D model and a learned statistical facial texture model. Rather than obtaining a person-specific facial surface, the goal of this work is to compute person-specific 3D face shape in terms of a few control parameters that are used by many applications. The proposed holistic approach estimates the 3D pose parameters as well as the face shape control parameters by registering the warped texture to a statistical face texture, which is carried out by a stochastic and genetic optimizer. The proposed approach has several features that make it very attractive: (i) it uses a single grey-scale image, (ii) it is person-independent, (iii) it is featureless (no facial feature extraction is required), and (iv) its learning stage is easy. The proposed approach lends itself nicely to 3D face tracking and face gesture recognition in monocular videos. We describe extensive experiments that show the feasibility and robustness of the proposed approach.
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