Facial Feature Tracking via Evolutionary Multiobjective Optimization

2010 ◽  
Vol 1 (1) ◽  
pp. 57-71 ◽  
Author(s):  
Eric C. Larson ◽  
Gary G. Yen
Keyword(s):  
Feature Tracking ◽  
Signal To Noise Ratio ◽  
Facial Animation ◽  
Facial Feature ◽  
Low Bit Rate ◽  
The Past ◽  
Facial Feature Tracking ◽  
Rate Coding ◽  
Facial Animation Parameters ◽  
Efficiency And Reliability

Facial feature tracking for model–based coding has evolved over the past decades. Of particular interest is its application in very low bit rate coding in which optimization is used to analyze head and shoulder sequences. We present the results of a computational experiment in which we apply a combination of non-dominated sorting genetic algorithm and a deterministic search to find optimal facial animation parameters at many bandwidths simultaneously. As objective functions are concerned, peak signal-to-noise ratio is maximized while the total number of facial animation parameters is minimized. Particularly, the algorithm is tested for efficiency and reliability. The results show that the overall methodology works effectively, but that a better error assessment function is needed for future study.

Facial Feature Tracking via Evolutionary Multiobjective Optimization

2012 ◽  
pp. 57-71
Author(s):  
Eric C. Larson ◽  
Gary G. Yen
Keyword(s):  
Feature Tracking ◽  
Computational Experiment ◽  
Signal To Noise Ratio ◽  
Facial Animation ◽  
Facial Feature ◽  
Low Bit Rate ◽  
Facial Feature Tracking ◽  
Rate Coding ◽  
Facial Animation Parameters ◽  
Efficiency And Reliability

Facial feature tracking for model–based coding has evolved over the past decades. Of particular interest is its application in very low bit rate coding in which optimization is used to analyze head and shoulder sequences. We present the results of a computational experiment in which we apply a combination of non-dominated sorting genetic algorithm and a deterministic search to find optimal facial animation parameters at many bandwidths simultaneously. As objective functions are concerned, peak signal-to-noise ratio is maximized while the total number of facial animation parameters is minimized. Particularly, the algorithm is tested for efficiency and reliability. The results show that the overall methodology works effectively, but that a better error assessment function is needed for future study.

A Robust Facial Feature Tracking Method Based on Optical Flow and Prior Measurement

2010 ◽  
Vol 4 (4) ◽  
pp. 62-75
Author(s):  
Guoyin Wang ◽  
Yong Yang ◽  
Kun He
Keyword(s):  
Optical Flow ◽  
Feature Tracking ◽  
Facial Feature ◽  
Recognition System ◽  
Research Area ◽  
Expression Recognition ◽  
Tracking Method ◽  
Facial Feature Tracking ◽  
Recognition Result ◽  
Method Of Measurement

Cognitive informatics (CI) is a research area including some interdisciplinary topics. Visual tracking is not only an important topic in CI, but also a hot topic in computer vision and facial expression recognition. In this paper, a novel and robust facial feature tracking method is proposed, in which Kanade-Lucas-Tomasi (KLT) optical flow is taken as basis. The prior method of measurement consisting of pupils detecting features restriction and errors and is used to improve the predictions. Simulation experiment results show that the proposed method is superior to the traditional optical flow tracking. Furthermore, the proposed method is used in a real time emotion recognition system and good recognition result is achieved.

Facial feature tracking with the super image vector inner product

2007 ◽  
Author(s):  
Wei Su ◽  
Laurence G. Hassebrook ◽  
S. Hariharan
Keyword(s):  
Feature Tracking ◽  
Facial Feature ◽  
Inner Product ◽  
Facial Feature Tracking
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