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.
Axel-Christian Guei and Moulay A. Akhloufi, "Deep learning for face recognition at a distance," Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520T (Presented at SPIE Defense + Security: April 18, 2018; Published: 9 May 2018); https://doi.org/10.1117/12.2304896.
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