A randomized deep neural network for emotion recognition with landmarks detection

In this paper, we present an innovative deep neural architecture employing parameter randomization in a complex classification model for emotion recognition. Actually, randomized deep neural networks represent an interesting alternative to exploring the efficiency-to-accuracy balance in real-life applications. Moreover, we also introduce the use of input frames composed of 468 facial landmarks coordinates and an innovative sampling procedure avoiding padding. The proposed randomized classifier is trained for emotion recognition on video sequences and the related accuracy is compared with a non-randomized version of the same model and with well-known benchmark architectures, demonstrating the robustness of the proposed approach in terms of classification accuracy and training time.