In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.

In this paper a novel application of multimodal emotion recognition algorithms in software engineering is described. Several application scenarios are proposed concerning program usability testing and software process improvement. Also a set of emotional states relevant in that application area is identified. The multimodal emotion recognition method that integrates video and depth channels, physiological signals and input devices usage patterns is proposed and some preliminary results on learning set creation are described.

In this paper an original approach is presented for facial expression and emotion recognition based only on depth channel from Microsoft Kinect sensor. The emotional user model contains nine emotions including the neutral one. The proposed recognition algorithm uses local movements detection within the face area in order to recognize actual facial expression. This approach has been validated on Facial Expressions and Emotions Database using 169 recordings of 25 persons. Though an average recognition accuracy is slightly above 50% this approach is highly independent of illumination conditions and also accepts low distance between sensor and the user. Thus, the proposed approach can be used to support other algorithms based on optical channel, as well as using skeleton or face tracking information.

The paper concerns accuracy of emotion recognition from facial expressions. As there are a couple of ready off-the-shelf solutions available in the market today, this study aims at practical evaluation of selected solutions in order to provide some insight into what potential buyers might expect. Two solutions were compared: FaceReader by Noldus and Xpress Engine by QuantumLab. The performed evaluation revealed that the recognition accuracies differ for photo and video input data and therefore solutions should be matched to the specificity of the application domain.