IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Image Analysis by Unsupervised Learning
Face Image Analysis by Unsupervised Learning
Facial expression recognition based on two dimensions without neutral expressions
ICEC'05 Proceedings of the 4th international conference on Entertainment Computing
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This paper presents a new approach method to recognize facial expressions in various internal states using manifold learning (ML). The manifold learning of facial expressions reflects the local features of facial deformations such as concavities and protrusions. We developed a representation of facial expression images based on manifold learning for feature extraction of facial expressions. First, we propose a zero-phase whitening step for illumination-invariant images. Second, facial expression representation from locally linear embedding (LLE) was developed. Finally, classification of facial expressions in emotion dimensions was generated on two dimensional structure of emotion with pleasure/displeasure dimension and arousal/sleep dimension. The proposed system maps facial expressions in various internal states into the embedding space described by LLE. We explore locally linear embedding space as a facial expression space in continuous dimension of emotion.