A Distillation Learning Model of Adaptive Structural Deep Belief Network for AffectNet: Facial Expression Image Database
This work addresses the challenge of ambiguous facial expression recognition for computer vision applications, but it is incremental as it builds on existing adaptive structure learning methods.
The paper tackled the problem of classifying ambiguous facial expressions in the AffectNet dataset by proposing a distillation learning model of an Adaptive Deep Belief Network, which improved classification accuracy from 78.4% to 91.3%.
Deep Learning has a hierarchical network architecture to represent the complicated feature of input patterns. We have developed the adaptive structure learning method of Deep Belief Network (DBN) that can discover an optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm, and can obtain the appropriate number of hidden layers in DBN. In this paper, our model is applied to a facial expression image data set, AffectNet. The system has higher classification capability than the traditional CNN. However, our model was not able to classify some test cases correctly because human emotions contain many ambiguous features or patterns leading wrong answer by two or more annotators who have different subjective judgment for a facial image. In order to represent such cases, this paper investigated a distillation learning model of Adaptive DBN. The original trained model can be seen as a parent model and some child models are trained for some mis-classified cases. For the difference between the parent model and the child one, KL divergence is monitored and then some appropriate new neurons at the parent model are generated according to KL divergence to improve classification accuracy. In this paper, the classification accuracy was improved from 78.4% to 91.3% by the proposed method.