14 May 2018 Face-It-Up: a scientific app for face processing using mobile devices and machine learning APIs
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Image Processing and Computer Vision solutions have become commodities for software developers, thanks to the growing availability of Application Programming Interfaces (APIs) that encapsulate rich functionality, powered by advanced algorithms. Tech giants like Apple, Google, IBM, and Microsoft have made APIs and micro-services available in the cloud for the agile integration of machine learning and intelligent features onto everyday applications. As privacy and cyber welfare become prime concerns, special efforts have been devoted in the field of face processing and recognition. In this context, this paper provides a friendly, intuitive and fun to use mobile app that leverages the state-of-the-art APIs for face, age, gender and emotion recognition. The Face- It-Up app was implemented for the iOS platform and uses the Microsoft Cognitive Services APIs as a tool for human vision and face processing research. Experimental work on image compression, upside-down orientation, the Thatcher effect, negative inversion, high frequency, facial artifacts, caricatures and image degradation were performed to test the application. For this purpose, we used the Radboud and 10k US Adult Faces Databases. The app benefits from accessing high-resolution imagery and touch input from the smart-devices, allowing for a wide range of new experiments from the user perspective. Furthermore, our approach serves as a potential framework for new initiatives in image-based biometrics, the Internet of Things, and citizen science.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Oge Marques, Oge Marques, Jhanon James, Jhanon James, Emilio Barcelos, Emilio Barcelos, "Face-It-Up: a scientific app for face processing using mobile devices and machine learning APIs", Proc. SPIE 10668, Mobile Multimedia/Image Processing, Security, and Applications 2018, 106680S (14 May 2018); doi: 10.1117/12.2307765; https://doi.org/10.1117/12.2307765


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