Robust Representation and Recognition of Facial Emotions Using Extreme Sparse Learning

Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition s...

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Bibliographic Details
Published in:IEEE transactions on image processing 2015-07, Vol.24 (7), p.2140-2152
Main Authors: Shojaeilangari, Seyedehsamaneh, Yau, Wei-Yun, Nandakumar, Karthik, Li, Jun, Teoh, Eam Khwang
Format: Article
Language:English
Subjects:
Algorithms
Dictionaries
Dictionary learning
Emotion recognition
Emotions - physiology
Extreme learning machine
Extreme sparse learning
Facial emotion
Facial Expression
Facial Recognition - physiology
Feature extraction
Head
Humans
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image reconstruction
Machine Learning
Pattern Recognition, Automated - methods
Photography - methods
Pose-invariance
Reproducibility of Results
Robustness
Sensitivity and Specificity
Sparse representation
Subtraction Technique
Vectors
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Summary:Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2015.2416634