Recognizing facial actions using Gabor wavelets with neutral face average difference

Authors:
Juliano J. Bazzo;Marcus V. Lamar
Affiliations:
Federal University of Paranà, Dept. of Electrical Eng., Curitiba, Brazil;Federal University of Paranà, Dept. of Electrical Eng., Curitiba, Brazil
Venue:
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Year:
2004

Citing 7
Cited 3

Coding, Analysis, Interpretation, and Recognition of Facial Expressions

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Classifying Facial Actions

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Comparison Between Geometry-Based and Gabor-Wavelets-Based Facial Expression Recognition Using Multi-Layer Perceptron

FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Evaluation of Gabor-Wavelet-Based Facial Action Unit Recognition in Image Sequences of Increasing Complexity

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Recognition of Asymmetric Facial Action Unit Activities and Intensities

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Eigenfaces for recognition

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Rapid and Brief Communication: Recognizing facial action units using independent component analysis and support vector machine

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Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships

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Facial expression recognition system based on rigid and non-rigid motion separation and 3D pose estimation

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Abstract

This paper describes a new pre-processing step to classify facial expression. Previous works suggest that Gabor wavelets applied to recognize facial expression images subtracted from neutral face from the same subject could achieve good recognition rate under controlled condition as eye and month alignment. We propose a recognition system where the Gabor kernels are applied on facial expression subtracted from a averaged neutral face. A fast pre-processing technique that generates a small dimension output data is also proposed. A correct recognition rate of 86.6% is obtained in a 7 upper face actions and 81.6% in a 7 lower face actions detection problem using a Neural Network based classifier. The performance is evaluated in a heterogeneous subject database with head motion and lighting variations.