Abstract. Nowadays, the trend of the latest research in face recognition model shows that “the complex - the better” paradigm can be directly applied to these systems, whose accuracy effectively depends on both a large number of well-trained parameters and a complex functional structure. If this approach is sustainable for an offline processing on a consumer PC, it is far less appealing in the mobile environment, where processing power, as well as a high amount of onboard RAM could not be available. The distillation technique, applied on the cumbersome dlib-resnet-v1 face recognition model results in a lighter version that, while maintaining a comparable accuracy, can achieve a faster processing rate (>10x) and a lower memory occupation (1/6). The final model has been implemented on a single board PC, also using a neural hardware accelerator
