Abstract—The ability to process human face information is crucial in many areas of government, business, and social media. Facial recognition provides businesses with the ability to provide services that include security, robotics, analysis, human resources, mobile applications, and user interfaces. Users can access their accounts and sign off transactions online just by taking a ‘selfie’. Machine Learning algorithms have been developed for face detection in media such as picture images. To recognise a face, the camera software must first detect it and identify the features before making an identification. Face detection is the first step of face recognition. In this research, the face detection APIs from five of the top public cloud vendors of facial recognition software have been tested and evaluated to establish which vendor performs the best for accuracy and to find any significant differences between the vendor APIs. The attributes tested were ‘Gender’ and ‘Age’. Surprisingly, the vendor Amazon Rekognition, IBM and FaceX only offered the attribute age as a range value rather than committing to an exact age. This immediately diminishes the accuracy of their respective APIs. The research proves the weaknesses in API accuracy by testing the resilience of the vendor APIs against degraded images. Azure was the overall winner with Rekognition in second place, Kairos in third, fourth place was IBM and FaceX took last place.
Index Terms—algorithms, API, dataset, face detection, face recognition
Cite: Ashling Malone and John Burns, "Evaluating the Accuracy of Public Cloud Vendor Face Detection API's," Journal of Image and Graphics, Vol. 9, No. 1, pp. 20-26, March 2021. doi: 10.18178/joig.9.1.20-26
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