Margin based knowledge distillation for mobile face recognition

Dmitry Nekhaev; Sergey Milyaev; Ivan Laptev

doi:10.1117/12.2557244

31 January 2020 Margin based knowledge distillation for mobile face recognition

Dmitry Nekhaev, Sergey Milyaev, Ivan Laptev

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330O (2020) https://doi.org/10.1117/12.2557244
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

With the rapid progress of face recognition it has more and more applications in everyday life. Although its backbone, very deep neural networks, also show improvement both in terms of accuracy and efficiency their computational cost and memory usage is still a limiting factor for deploying these models on a hardware with limited computational and power resources, such as mobile or embedded devices. Here arises the task of learning fast and compact deep neural networks which have a comparable accuracy to the complex model as requirement of real-life applications. Another issue is that sometimes face recognition system may run models of different complexity depending of the devices used for biometric template extraction (i.e. desktop with GPU or mobile phone), so the compatibility between the face descriptors is desirable. Our paper considers both this cases: we propose a new method for learning fast and compact face recognition model which has a similar performance to a much more complex model used for transferring its knowledge and we also show that both these models can be used for verification in a single face recognition system. To the best of our knowledge such evaluation of a compatibility between 2 different models for face recognition was never done before our work.

Citation Download Citation

Dmitry Nekhaev, Sergey Milyaev, and Ivan Laptev "Margin based knowledge distillation for mobile face recognition", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330O (31 January 2020); https://doi.org/10.1117/12.2557244

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available