IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
A similarity-based neural network for facial expression analysis
Pattern Recognition Letters
Zero knowledge hidden Markov model inference
Pattern Recognition Letters
Speech enhancement with inventory style speech resynthesis
IEEE Transactions on Audio, Speech, and Language Processing
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Neural networks (NNs) are often combined with Hidden Markov Models (HMMs) in speech recognition for achieving superior performance. In this paper, this hybrid approach is employed in facial emotion classification. Gabor wavelets are employed to extract features from difference images obtained by subtracting the first frame showing a frontal face from the current frame. The NN, which takes the form of Multilayer perceptron (MLP), is used to classify the feature vector into different states of a HMM of a certain emotion sequence, i.e., neutral, intermediate and peak. In addition to using 1-0 as targets for the NN, a heuristic strategy of assigning variable targets 1-x-0 has also been applied. After training, we interpret the output values of the NN as the posterior of the HMM state and directly apply the Viterbi algorithm to these values to estimate the best state path. The experiments show that with variable targets for the NN, the HMM gives better results than that with 1-0 targets. The best HMM results are obtained for x = 0.8 in 1-x-0.