Real-Time Expression Recognition System Using Active Appearance Model and EFM

2007 ◽  
pp. 1078-1084 ◽  
Author(s):  
Kyoung-Sic Cho ◽  
Yong-Guk Kim ◽  
Yang-Bok Lee
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Expression Recognition ◽  
Active Appearance

scholarly journals Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Jinwei Wang ◽  
Xirong Ma ◽  
Yuanping Zhu ◽  
Jizhou Sun
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Parallel Implementation ◽  
Model Fitting ◽  
Parallel Architecture ◽  
High Dimensional ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Fitting Algorithm ◽  
Active Appearance

The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.

scholarly journals Real-time 3D face tracking based on active appearance model constrained by depth data

2014 ◽  
Vol 32 (11) ◽  
pp. 860-869 ◽  
Author(s):  
Nikolai Smolyanskiy ◽  
Christian Huitema ◽  
Lin Liang ◽  
Sean Eron Anderson
Keyword(s):  
Real Time ◽  
Face Tracking ◽  
Appearance Model ◽  
Active Appearance Model ◽  
3D Face ◽  
Depth Data ◽  
3D Face Tracking ◽  
Active Appearance

Geometrical Features and Active Appearance Model Applied to Facial Expression Recognition

2016 ◽  
Vol 16 (04) ◽  
pp. 1650019 ◽  
Author(s):  
Flávio Altinier Maximiano da Silva ◽  
Helio Pedrini
Keyword(s):  
Gabor Filter ◽  
Support Vector ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Data Sets ◽  
Expression Recognition ◽  
Basic Emotions ◽  
Training Support ◽  
In The Wild ◽  
Active Appearance

One of the most effective ways of expressing emotion is through facial expressions. This work proposes and discusses a geometrical descriptor based on the calculation of distances from coordinates of facial fiducial points, which are used as features for training support vector machines (SVM) to classify emotions. Three data sets are studied and six basic emotions are considered in our experiments. In comparison to other approaches available in the literature, the results obtained with our geometrical descriptor demonstrated to be very competitive, achieving high classification F-score rates. Additionally, we evaluate whether the combination of our geometrical descriptor with an appearance feature, the Gabor filter, allows emotions to be even more distinguishable for the classifier. The result is positive for two out of three data sets. Finally, to simulate in-the-wild scenarios, an active appearance model (AAM) is trained to position the fiducial points on the correct facial locations, instead of using the ones provided by the data sets. As the fitting error is considered acceptable, the former experiments are also conducted with the new data generated by the AAM. The results show a small drop on the F-score values when compared to the data originally provided by the data sets,but are still satisfactory.

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