A facial feature tracker for human-computer interaction based on 3D Time-Of-Flight cameras

Böhme, Martin; Haker, Martin; Martinetz, Thomas; Barth, Erhardt

doi:10.1504/ijista.2008.021289

Cited by 18 publications

(15 citation statements)

References 9 publications

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“…Figure 5. Example nose-detection results shown on ToF-intensity (left) and ToF-range image (right); detection error rate is 0,03 [4]. A MESA SR3000 cameras has been used.…”

Section: Dynamic 3d Depth Keyingmentioning

confidence: 99%

ToF-sensors: New dimensions for realism and interactivity

Kolb

Barth²,

Koch

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

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A growing number of applications depend on accurate and fast 3D scene analysis. Examples are object recognition, collision prevention, 3D modeling, mixed reality, and gesture recognition. The estimation of a range map by image analysis or laser scan techniques is still a timeconsuming and expensive part of such systems.A lower-priced, fast and robust alternative for distance measurements are Time-of-Flight (ToF) cameras. Recently, significant improvements have been made in order to achieve low-cost and compact ToF-devices, that have the potential to revolutionize many fields of research, including Computer Vision, Computer Graphics and Human Computer Interaction (HCI).These technologies are starting to have an impact on research and commercial applications. The upcoming generation of ToF sensors, however, will be even more powerful and will have the potential to become "ubiquitous geometry devices" for gaming, web-conferencing, and numerous other applications. This paper will give an account of some recent developments in ToF-technology and will discuss applications of this technology for vision, graphics, and HCI.

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“…Figure 5. Example nose-detection results shown on ToF-intensity (left) and ToF-range image (right); detection error rate is 0,03 [4]. A MESA SR3000 cameras has been used.…”

Section: Dynamic 3d Depth Keyingmentioning

confidence: 99%

ToF-sensors: New dimensions for realism and interactivity

Kolb

Barth²,

Koch

2008

2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Self Cite

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“…Visual tracking is integrated into the novel FCIN system for measuring the position and orientation of a head (Asamwar et al, 2010;Toure and Beiji, 2010;Bohme et al, 2008;Chan et al, 2010;Attarzadeh and Ow, 2010). .…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Head positioning using Fixed Center Interpolation Net with Weight Elimination Algorithm

Iskandarani¹

2011

Journal of Computer Science

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A reliable algorithm for head movements inside a vehicle is designed. The proposed algorithm allowed the adjustment of basic functions such as indicators, mirrors and reverse lights based on the driver final head position. The algorithm system mapped a predefined coordinates for driver's head that resulted in a computable geometry via a sensory system which is fed to the vehicle actuating system. Problem statement: Head position recognition is one of the most common problems encountered in engineering and scientific disciplines, which involves developing prediction or classification models from historic data or training samples. In the past few years face detection and person identification became important issues due to security concerns, leading to head gesture algorithm development and implementation. Approach: This study introduces a new approach that combines Fixed Center Interpolation Net Algorithm (FCIN) with Wight Elimination Algorithm (WEA). This enhances the ability to classify and predict head positions and poses and gives better representation capabilities for the overall system algorithm. Such algorithm is able to handle pattern recognition problems using Radial Basis Function (RBF) models. The system algorithm has been developed based on the mathematical properties of the interpolation and design matrices of RBF models. Results: A reliable, fast and robust approach for driver head position recognition is achieved and presented. Conclusion: A simple hybrid algorithm for driver's head movements is designed and tested. The obtained results proved the algorithm applicability and ability to predict and act upon head gestures.

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“…Based on the scope of each application area, various aspects of human motion have been studied from different perspectives. For example, motor control and learning theorists have studied how the central nervous system creates and updates internal representations of limb dynamics, to perform complex, programmed movements in dynamic environments (Lin et al, 2007;Eisenstein et al, 2008;Suk et al, 2010;Tonga et al, 2007;Wu and Trivedi, 2008;Markin and Prakash, 2006;Choi and Kim, 2008;Bohme et al, 2008). Sports analysts have studied the relationship between human physiology and movement efficiency.…”

Section: Introductionmentioning

confidence: 99%

Head Gesture Analysis using Matrix Group Displacement Algorithm

Iskandarani¹

2010

Journal of Computer Science

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A novel algorithm for head gestures interpretation is designed and tested. The designed system carries out gesture detection and recognition using the MGDA algorithm, which implements random sampling and importance sampling, such technique can track head poses and estimate head positions. Problem statement: Head position is an important indicator of a person's focus of attention, which can be used as a key for multi-view face analysis assuming that face recognition and identification to be viewed dependently. This will help in selecting the best view model. Also, in the past few years face detection and person identification became important issues due to security concerns, leading to head gesture algorithm development and implementation. Approach: The captured image was allocated a map after which a file conversion process is carried out, allowing the next stage of image data conversion of head poses to be applied. This results in a specific number of matrices per pose that hold the necessary information. Such information was then allocated sequences representing head gesture poses which is combined for classification and correlation purposes to regenerate a predicted time reconstructed continuous movements. Results: A reliable, fast and robust approach for static head gesture recognition was achieved and presented. Conclusion: This very successful approach to head pose detection and gesture classification is strongly supported by its ability to correlate different signal input technologies as the devised algorithm can accommodate different inputs.

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A facial feature tracker for human-computer interaction based on 3D Time-Of-Flight cameras

Cited by 18 publications

References 9 publications

ToF-sensors: New dimensions for realism and interactivity

ToF-sensors: New dimensions for realism and interactivity

A Novel Approach to Head positioning using Fixed Center Interpolation Net with Weight Elimination Algorithm

Head Gesture Analysis using Matrix Group Displacement Algorithm

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