Multi-level feature fusion for group-level emotion recognition

Abstract

In this paper, the influence of low-level and mid-level features is investigated for image-based group emotion recognition. We hypothesize that the human faces, and the objects surrounding them are major sources of information and thus can serve as mid-level features. Hence, we detect faces and objects using pre-trained Deep Net models. Information from different layers in conjunction with different encoding techniques is extensively investigated to obtain the richest feature vectors. The best result obtained classification accuracy of 65.0% on the validation set, is submitted to the Emotion Recognition in the Wild (EmotiW 2017) group-level emotion recognition sub-challenge. The best feature vector yielded 75.1% on the testing set. Post competition, few more experiments were performed and included the same