Extracting AudioVisual Features for Emotion Recognition Through Active Feature Selection Research in automatic emotion recognition has seldom addressed the issue of computational resource utilisation. With the advent of ambient technology, which employs a variety of lowpower, resource constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions aim at maintaining the userxe2x80x99s independence as unobtrusively as possible. In this context, efforts are being made to model human social signals such as affects using lowcost technologies, which can aid health monitoring. This paper presents an Active Feature Selection (AFS) method using selforganized maps neural networks for emotion recognition in the wild. The AFS is used for feature subsets selection from three different feature sets: 62 out of 88 features were selected for eGeMAPs, 21 out of 988 for emobase, and 140 out of 2832 for LBPTOP features. The results show that the features subsets selected by AFS provide better results than the entire feature set and PCA dimensionality reduction method. The best improvement is observed on emobase features, followed by eGeMAPs. For visual features, nearly the same results are obtained with a significant reduction in dimensionality (only 5% of the full feature set is required for the same level of accuracy). The weighted score fusion results in an improvement, leading to 43.40% and 40.12% accuracies on the EmotiW 2018 validation and test datasets respectively.

MultiChannel Physiological Signal Emotion Recognition Based on ReliefF Feature Selection Emotion recognition plays a very important role nowadays. Emotional recognition of electroencephalogram (EEG) signals involves highdimensional EEG data, which is still a challenging task now. This paper presents an emotion recognition framework based on EEG and peripheral signals, and the ReliefF algorithm is used to select features of EEG and peripheral signals. The DEAP data set is used to process multichannel physiology signals. In this paper the human emotion is classified into two categories (happy, unhappy) and three categories (happy, neutral, unhappy), respectively. The performance of the proposed method is evaluated in conjunction with ReliefF algorithm. The best average accuracy of KNearest Neighbor (KNN) and random forest (RF) are 94.295% and 98.832% in the binary classification tasks, and 79.210% and 98.364% in the triple classification tasks, respectively. At the same time, we also have adopted F1score as the metric in the binary classification, which are 93.944% and 98.835%. The evaluation on the DEAP data set shows that using the ReliefF algorithm for feature selection can achieve good results on the issue of physiological emotion recognition.