Facial expressions recognition (FER) plays a much important role in various applications from human-computer interfaces to psychological tests. However, most methods are confronted with the quality of the face images, vanishing gradients problem, over-trained problem, difference of face images such as in age and ethnicity, mulitple parameters required tuning, and dubious class labels in the training data. These negative factors largely hurt the recognition performance. To alleviate these problems, this paper proposes an new approach named ensemble convolutional echo state network, which takes Echo State Network (ESN) as the base classifier for ensemble and Convolutional Network (CN) to transform the input face image for further feeding to ESN, where the random parameters and architectures are assigned to ensure the diversity of the ensemble and to avoid computing stochastic gradient. Based on the rich dynamics of ESN and rich variations of input face image finished by CN, the proposed approach has the great ability to deal with the real facial expression recognition and to be scaled to the larger training data. It has also only one parameter to be adjusted. Conducted experiments show that the method achieves significant improvement over current methods on person-independent facial expression recognition.
['Guihua Wen', 'Huihui Li', 'Danyang Li']
An ensemble convolutional echo state networks for facial expression recognition