Databases and Information Systems Commons^™

Assessing The Accuracy Of Four Popular Face Recognition Tools For Inferring Gender, Age, And Race, Soon-Gyu Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

In this research, we evaluate four widely used face detection tools, which are Face++, IBM Bluemix Visual Recognition, AWS Rekognition, and Microsoft Azure Face API, using multiple datasets to determine their accuracy in inferring user attributes, including gender, race, and age. Results show that the tools are generally proficient at determining gender, with accuracy rates greater than 90%, except for IBM Bluemix. Concerning race, only one of the four tools provides this capability, Face++, with an accuracy rate of greater than 90%, although the evaluation was performed on a high-quality dataset. Inferring age appears to be a challenging problem, as …