A Comparative Study on Detection Accuracy of Cloud-Based Emotion Recognition Services

The ability of software systems adapting to human's input is a key element in the symbiosis of human-system co-adaptation, where human and software-based systems work together in a close partnership to achieve synergetic goals. This seamless integration will eliminate the barriers between human and machine. A critical requirement for co-adaptive systems is software system's ability to recognize human emotion, in which the system can detect and interpret users' emotions and adapt accordingly. There are numerous solutions that provide the technologies for emotion recognition. However, selecting an appropriate solution for a given task within a specific application domain can be challenging. The vast variation between these solutions makes the selecting task even more difficult. This paper compares cloud-based emotion recognition services offered by Amazon, Google, and Microsoft. These services detect human emotion through facial expression recognition with the utilization of computer vision. The focus of this paper is to measure the detection accuracy of these services. Accuracy is calculated based on the highest confidence rating returned by each service. All three services have been tested with the same dataset. This paper concludes with findings and recommendations based on our comparative analysis among these services.