Performance Analysis and Evaluation of Cloud Vision Emotion APIs
This work provides an incremental evaluation of existing cloud services for emotion recognition, relevant for developers and researchers in human-computer interaction.
The study compared the performance of two cloud-based vision APIs for emotion recognition from facial images using a public dataset of 980 images, finding that prediction accuracy varied significantly between services and across different emotion classes.
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.