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AffectNet contains more than 1,000,000 facial images which manually annotatedfor the presence of eight discrete facial expressions and the intensity ofvalence and arousal. Adaptive structural learning method of DBN (Adaptive DBN)is positioned as a top Deep learning model of classification capability forsome large image benchmark databases. The Convolutional Neural Network andAdaptive DBN were trained for AffectNet and classification capability wascompared. Adaptive DBN showed higher classification ratio. However, the modelwas not able to classify some test cases correctly because human emotionscontain many ambiguous features or patterns leading wrong answer which includesthe possibility of being a factor of adversarial examples, due to two or moreannotators answer different subjective judgment for an image. In order todistinguish such cases, this paper investigated a re-learning model of AdaptiveDBN with two or more child models, where the original trained model can be seenas a parent model and then new child models are generated for somemisclassified cases. In addition, an appropriate child model was generatedaccording to difference between two models by using KL divergence. Thegenerated child models showed better performance to classify two emotioncategories: `Disgust' and `Anger'.

Re-learning of Child Model for Misclassified data by using KL Divergence in AffectNet: A Database for Facial Expression