Sensitivity to Emotion Intensity and Recognition of Emotion Expression in Neurotypical Children

This study assessed two components of face emotion processing: emotion recognition and sensitivity to intensity of emotion expressions and their relation in children age 4 to 12 (N = 216). Results indicated a slower development in the accurate decoding of low intensity expressions compared to high intensity. Between age 4 and 12, children discriminated high intensity expressions better than low ones. The intensity of expression had a stronger impact on overall face expression recognition. High intensity happiness was better recognized than low intensity up to age 11, while children 4 to 12 had difficulties discriminating between high and low intensity sadness. Our results suggest that sensitivity to low intensity expressions acts as a complementary mediator between age and emotion expression recognition, while this was not the case for the recognition of high intensity expressions. These results could help in the development of specific interventions for populations presenting socio-cognitive and emotion difficulties.

Concise CNN model for face expression recognition

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.