Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Automated Facial Expression Recognition Based on FACS Action Units
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Robust Face Tracking Using Color
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
A Unified Learning Framework for Real Time Face Detection and Classification
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
EyeKeys: A Real-Time Vision Interface Based on Gaze Detection from a Low-Grade Video Camera
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 10 - Volume 10
Democratic Integration: Self-Organized Integration of Adaptive Cues
Neural Computation
Fast rotation invariant multi-view face detection based on real adaboost
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Adaboost with totally corrective updates for fast face detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Improvements in vision-based pointer control
Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility
Detecting learner frustration: towards mainstream use cases
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
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Accurate head tilt detection has a large potential to aid people with disabilities in the use of human-computer interfaces and provide universal access to communication software. We show how it can be utilized to tab through links on a web page or control a video game with head motions. It may also be useful as a correction method for currently available video-based assistive technology that requires upright facial poses. Few of the existing computer vision methods that detect head rotations in and out of the image plane with reasonable accuracy can operate within the context of a real-time communication interface because the computational expense that they incur is too great. Our method uses a variety of metrics to obtain a robust head tilt estimate without incurring the computational cost of previous methods. Our system runs in real time on a computer with a 2.53 GHz processor, 256 MB of RAM and an inexpensive webcam, using only 55% of the processor cycles.