  This paper focuses on the design, deployment and evaluation of Convolutional
Neural Network (CNN) architectures for facial affect analysis on mobile
devices. Unlike traditional CNN approaches, models deployed to mobile devices
must minimise storage requirements while retaining high performance. We
therefore propose three variants of established CNN architectures and
comparatively evaluate them on a large, in-the-wild benchmark dataset of facial
images. Our results show that the proposed architectures retain similar
performance to the dataset baseline while minimising storage requirements:
achieving 58% accuracy for eight-class emotion classification and average RMSE
of 0.39 for valence/arousal prediction. To demonstrate the feasibility of
deploying these models for real-world applications, we implement a music
recommendation interface based on predicted user affect. Although the CNN
models were not trained in the context of music recommendation, our case study
shows that: (i) the trained models achieve similar prediction performance to
the benchmark dataset, and (ii) users tend to positively rate the song
recommendations provided by the interface. Average runtime of the deployed
models on an iPhone 6S equates to ~45 fps, suggesting that the proposed
architectures are also well suited for real-time deployment on video streams.
