Benchmarking lightweight face architectures on specific face recognition scenarios

This paper studies the impact of lightweight face models on real applications. Lightweight architectures proposed for face recognition are analyzed and evaluated on different scenarios. In particular, we evaluate the performance of five recent lightweight architectures on five face recognition scenarios: image and video based face recognition, cross-factor and heterogeneous face recognition, as well as active authentication on mobile devices. In addition, we show the lacks of using common lightweight models unchanged for specific face recognition tasks, by assessing the performance of the original lightweight versions of the lightweight face models considered in our study. We also show that the inference time on different devices and the computational requirements of the lightweight architectures allows their use on real-time applications or computationally limited platforms. In summary, this paper can serve as a baseline in order to select lightweight face architectures depending on the practical application at hand. Besides, it provides some insights about the remaining challenges and possible future research topics.