Happiness level prediction with sequential inputs via multiple regressions

This paper presents our solution submitted to the Emotion Recognition in the Wild (EmotiW 2016) group-level happiness intensity prediction sub-challenge. The objective of this sub-challenge is to predict the overall happiness level given an image of a group of people in a natural setting. We note that both the global setting and the faces of the individuals in the image influence the group-level happiness intensity of the image. Hence the challenge lies in building a solution that incorporates both these factors and also considers their right combination. Our proposed solution incorporates both these factors as a combination of global and local information. We use a convolutional neural network to extract discriminative face features, and a recurrent neural network to selectively memorize the important features to perform the group-level happiness prediction task. Experimental evaluations show promising performance improvements, resulting in Root Mean Square Error (RMSE) reduction of about 0.5 units on the test set compared to the baseline algorithm that uses only global information.