Comparison Of Multilayer Perceptron And Radial Basis Function Neural Networks For EMG-Based Facial Gesture Recognition

Mahyar Hamedi, Sh-Hussain Salleh, Mehdi Astaraki, Alias Mohd Noor, Arief Ruhullah A. Harris
The 8th International Conference on Robotic, Vision, Signal Processing & Power Applications Lecture Notes in Electrical Engineering Volume 291, 2014, pp 285-294


This paper compared the application of multilayer perceptron (MLP) and radial basis function (RBF) neural networks on a facial gesture recognition system. Electromyogram (EMG) signals generated by ten different facial gestures were recorded through three pairs of electrodes. EMGs were filtered and segmented into non-overlapped portions. The time-domain feature mean absolute value (MAV) and its two modified derivatives MMAV1 and MMAV2 were extracted. MLP and RBF were used to classify the EMG features while six types of activation functions were evaluated for MLP architecture. The discriminating power of single/multi features was also investigated. The results of this study showed that symmetric saturating linear was the most effective activation function for MLP; the feature set MAV + MMAV1 provided the highest accuracy by both classifiers; MLP reached higher recognition ratio for most of features; RBF was the faster algorithm which also offered a reliable trade-off between the two key metrics, accuracy and time.