F. Albu, D. Hagiescu, M. Puica, L. Vladutu, “Neural Network Approaches for Children's Emotion Recognition In Intelligent Learning Applications”,
accepted EDULEARN 2015, Barcelona, Spain.
In this paper the children’s emotion recognition performance of several neural networks approaches is described. The Radial Basis Function (RBF), Probabilistic Neural Networks (PNN), Extreme Learning Machines (ELM) and Support Vector Machines (SVM) variants were tested on recorded speech signals and face detected images.
For the speech signal the Mel Frequency Cepstral Coefficients (MFCC) and other parameters were computed together with their mean and standard deviation in order to obtain the feature vector for the neural network input.
For images, input parameters for emotion detection consisted in several distances computed between certain facial features using space coordinates for eyes, eyebrows and lips.
In case of RBF networks and speech signals we investigated the influence of the number of centres chosen by the k-means algorithm on the recognition performance on both training and test databases. The FAU Aibo Emotion corpus database was used because it has recordings from 51 children aged 10 to 13 years while interacting with a Sony Aibo robot. It is shown that there is a limitation of performance over a certain number of centres for the chosen identified emotions.
Another promising technique for classification of speech feature vectors is the use of ELM. They are Single-hidden Layer Feedforward Neural (SFLN) networks. In this case, random values are allocated to the weights of the hidden layer and the output weights are found by matrix operations.
Our simulations have shown a similar behaviour of ELM networks with the RBF networks. There is a limitation of ELM performance after an increase of the number of hidden neurons over a specific number. Also, it is shown that the variant called Online Sequential ELM (OS-ELM) obtains very close classification performance to that of ELM.
For facial emotion recognition a subset of 20 subjects ages 6 to 9 (10 boys and 10 girls) from The Dartmouth Database of Children's Faces was used. Different types of RBF networks (Classic RBF, Multi-Stage RBF, and Probabilistic Neural Networks) with variable number of hidden neurons were trained and tested.
SVMs are a new type of supervised nonlinear learning paradigms which were used in the last decades both for classification and regression analysis. They have shown similar performance to RBF networks in our emotion detection simulations.
The results prove the effectiveness of several neural networks techniques in estimating the children affective state that can have important implications on technology-enhanced learning and intelligent software applications for children. It is shown that child affective modelling it is as important as their cognitive modelling when it comes to deciding the next tutoring step and how it should be delivered.