Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Promising approaches have been reported, including automatic methods for facial and vocal affect recognition. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions-despite the fact that deliberate behavior differs in visual and audio expressions from spontaneously occurring behavior. Recently efforts to develop algorithms that can process naturally occurring human affective behavior have emerged. This paper surveys these efforts. We first discuss human emotion perception from a psychological perspective. Next, we examine the available approaches to solving the problem of machine understanding of human affective behavior occurring in real-world settings. We finally outline some scientific and engineering challenges for advancing human affect sensing technology.

A survey of affect recognition methods: audio, visual and spontaneous expressions

Affective and human-centered computing are two areas related to HCI which have attracted attention during the past years. One of the reasons that this may be attributed to, is the plethora of devices able to record and process multimodal input from the part of the users and adapt their functionality to their preferences or individual habits, thus enhancing usability and becoming attractive to users less accustomed with conventional interfaces. In the quest to receive feedback from the users in an unobtrusive manner, the visual and auditory modalities allow us to infer the users' emotional state, combining information both from facial expression recognition and speech prosody feature extraction. In this paper, we describe a multi-cue, dynamic approach in naturalistic video sequences. Contrary to strictly controlled recording conditions of audiovisual material, the current research focuses on sequences taken from nearly real world situations. Recognition is performed via a 'Simple Recurrent Network' which lends itself well to modeling dynamic events in both user's facial expressions and speech. Moreover this approach differs from existing work in that it models user expressivity using a dimensional representation of activation and valence, instead of detecting the usual 'universal emotions' which are scarce in everyday human-machine interaction. The algorithm is deployed on an audiovisual database which was recorded simulating human-human discourse and, therefore, contains less extreme expressivity and subtle variations of a number of emotion labels.

Modeling naturalistic affective states via facial and vocal expressions recognition

Affective and human-centered computing have attracted a lot of attention during the past years, mainly due to the abundance of devices and environments able to exploit multimodal input from the part of the users and adapt their functionality to their preferences or individual habits. In the quest to receive feedback from the users in an unobtrusive manner, the combination of facial and hand gestures with prosody information allows us to infer the users' emotional state, relying on the best performing modality in cases where one modality suffers from noise or bad sensing conditions. In this paper, we describe a multi-cue, dynamic approach to detect emotion in naturalistic video sequences. Contrary to strictly controlled recording conditions of audiovisual material, the proposed approach focuses on sequences taken from nearly real world situations. Recognition is performed via a 'Simple Recurrent Network' which lends itself well to modeling dynamic events in both user's facial expressions and speech. Moreover this approach differs from existing work in that it models user expressivity using a dimensional representation of activation and valence, instead of detecting discrete 'universal emotions', which are scarce in everyday human-machine interaction. The algorithm is deployed on an audiovisual database which was recorded simulating human-human discourse and, therefore, contains less extreme expressivity and subtle variations of a number of emotion labels.

/pdf/modeling-naturalistic-affective-states-via-facial-vocal-and-5bmvab5rbj.pdf

Modeling naturalistic affective states via facial, vocal, and bodily expressions recognition

An improved model is proposed by introducing the direction and time profit factors to Elman neural network (NN) and applied to the fields of the finial investing and atmospheric environment. Simulation results show the effectiveness of the proposed model for stock forecasting and the potential of the forecast and assessment for the atmospheric quality.

An improved elman neural network with profit factors and its applications

Data analysis and preprocessing techniques for air quality prediction: a survey

392P A phase II study of SAF-189s in patients with advanced ROS1 fusion-positive non-small cell lung cancer

The assessment and forecast for atmospheric quality have become the key problem in the study of the quality of atmospheric environment. In order to evaluate the grade of the atmospheric pollution, a model based on the particle swarm optimization (PSO) algorithm is proposed in this paper. Experimental results show the advantages of the proposed models, such as pellucid principle and physical explication, predigested formula and low computation complexity. In addition, an improved Elman neural network, namely, the output-input feedback Elman (OIF Elman) neural network is also applied to forecast the atmospheric quality. Simulations show that the OIF Elman neural network has great potential in the field of forecasting the atmospheric quality.

Applications of PSO Algorithm and OIF Elman Neural Network to Assessment and Forecasting for Atmospheric Quality

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392P A phase II study of SAF-189s in patients with advanced ROS1 fusion-positive non-small cell lung cancer

Applications of PSO Algorithm and OIF Elman Neural Network to Assessment and Forecasting for Atmospheric Quality