Learning to Understand New Facial Expressions

Abstract

Facial expression recognition is getting popular in the research community because of its extensive use in understanding human sentiments. Among various medium of human interaction uses in daily life, the facial expression is the most direct form of communication that explains a lot about human emotions. Because of this reason, researchers are actively exploiting this field of human-computer interaction. The research aims for the development of automatic facial expression annotation for context-based database generation. We pointed out the limitation of an existing facial expression detection system for real-world application and studied new ways to bridge current research and user application. We proposed a one-shot learning-based automatic facial expression labeling technique which requires very few manual labels to understand the context of sentiment in expression and utilizes them to train facial expression system with a specific use case. The evaluation of the proposed model is done with two methods (i) we manually labeled few more examples and tested the model against those examples, and (ii) from the seven basic facial expressions, we kept one facial expression separate and used those example to test the efficiency of the model.