University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Resolution Invariant Face Recognition using a Distillation Approach

Khalid, Syed, Awais, Muhammad, Feng, Zhenhua, Chan, Chi-Ho, Farooq, Ammarah and Kittler, Josef (2020) Resolution Invariant Face Recognition using a Distillation Approach IEEE Transactions on Biometrics, Behavior, and Identity Science.

TBIOM2020.pdf - Accepted version Manuscript

Download (1MB) | Preview


Modern face recognition systems extract face representations using deep neural networks (DNNs) and give excellent identification and verification results, when tested on high resolution (HR) images. However, the performance of such an algorithm degrades significantly for low resolution (LR) images. A straight forward solution could be to train a DNN, using simultaneously, high and low resolution face images. This approach yields a definite improvement at lower resolutions but suffers a performance degradation for high resolution images. To overcome this shortcoming, we propose to train a network using both HR and LR images under the guidance of a fixed network, pretrained on HR face images. The guidance is provided by minimising the KL-divergence between the output Softmax probabilities of the pretrained (i.e., Teacher) and trainable (i.e., Student) network as well as by sharing the Softmax weights between the two networks. The resulting solution is tested on down-sampled images from FaceScrub and MegaFace datasets and shows a consistent performance improvement across various resolutions. We also tested our proposed solution on standard LR benchmarks such as TinyFace and SCFace. Our algorithm consistently outperforms the state-of-the-art methods on these datasets, confirming the effectiveness and merits of the proposed method.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Awais, Muhammad
Chan, Chi-Ho
Date : 29 June 2020
Additional Information : Embargo OK Metadata Pending
Depositing User : James Marshall
Date Deposited : 01 Jul 2020 13:26
Last Modified : 01 Jul 2020 13:26

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800