Publication

Abstract : The research on automatic emotional recognition has been increased drastically because of its significant influence on various applications such as treatment of the illness, educational practices, decision making, and the development of commercial applications. Using Machine Learning (ML) models, we have been trying to determine the emotion accurately and precisely from the facial expressions. But it requires a colossal number of resources in terms of data as well as computational power and can be time-consuming during its training. To solve these complications, meta-learning has been introduced to train a model on a variety of learning tasks, which assists the model to generalize the novel learning tasks using a restricted amount of data. In this paper, we have applied one of the meta-learning techniques and proposed a model called MLARE(Meta Learning Approach to Recognize Emotions) that recognizes emotions using our in-house developed dataset AED-2 (Amrita Emotion Dataset-2) which has 56 images of subjects expressing seven basic emotions viz., disgust, sad, fear, happy, neutral, anger, and surprise. It involves the implementation of the Siamese network which estimates the similarity between the inputs. We could achieve 90.6% of overall average accuracy in recognizing emotions with the state-of-the-art method of one-shot learning tasks using the convolutional neural network in the Siamese network.