9 May 2018 Deep learning for face recognition at a distance
Author Affiliations +
Abstract
Face recognition is a research area that has been widely studied by the computer vision community in the past years. Most of the work deals with close frontal images of the face where facial structures can be easily distinguished. Little work deals with recognizing faces at a distance, where faces are at a very low resolution and barely distinguishable. In this work, we present a deep learning architecture that can be used to enhance lower resolution facial images captured at a distance. The proposed framework uses Deep Convolutional Generative Adversarial Networks (DCGAN). The proposed architecture works well even in the presence of a small number of images for learning. The new enhanced images are then sent to a face recognition algorithm for classification. The proposed framework outperforms classical enhancement techniques and leads to an increase in the face recognition performance.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Axel-Christian Guei, Axel-Christian Guei, Moulay A. Akhloufi, Moulay A. Akhloufi, "Deep learning for face recognition at a distance", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520T (9 May 2018); doi: 10.1117/12.2304896; https://doi.org/10.1117/12.2304896
PROCEEDINGS
11 PAGES + PRESENTATION

SHARE
RELATED CONTENT

Deep ear biometrics
Proceedings of SPIE (May 08 2018)
Local discriminant basis neural network ensembles
Proceedings of SPIE (April 04 2000)

Back to Top