Performance Analysis and Evaluation of Cloud Vision Emotion APIs

TL;DR

This study compares the performance of two cloud-based emotion recognition APIs using a dataset of facial images, highlighting variations in accuracy across different emotions and service providers.

Contribution

It provides a systematic evaluation of two popular cloud vision APIs for emotion detection, revealing their strengths and weaknesses across emotion classes.

Findings

Prediction accuracy varies by emotion and API.

Performance differences are significant across emotion classes.

Results inform selection of cloud APIs for emotion recognition tasks.

Abstract

Facial expression is a way of communication that can be used to interact with computers or other electronic devices and the recognition of emotion from faces is an emerging practice with application in many fields. There are many cloud-based vision application programming interfaces available that recognize emotion from facial images and video. In this article, the performances of two well-known APIs were compared using a public dataset of 980 images of facial emotions. For these experiments, a client program was developed which iterates over the image set, calls the cloud services, and caches the results of the emotion detection for each image. The performance was evaluated in each class of emotions using prediction accuracy. It has been found that the prediction accuracy for each emotion varies according to the cloud service being used. Similarly, each service provider presents a strong variation of performance according to the class being analyzed, as can be seen with more detail in this artilects.