Face recognition has a wide practical applicability in various contexts, for example, detecting students attending a lecture at university, identifying members in a gym or monitoring people in an airport. Recent methods based on Convolutional Neural Network (CNN), such as FaceNet, achieved state-of-the-art performance in face recognition. Inspired from this work, we propose a pipeline to improve face recognition systems based on Center loss. The main advantage is that our approach does not suffer from data expansion as in Triplet loss. Our pipeline is capable of cleaning an existing face dataset to improve the recognition performance or creating one from scratch. We present detailed experiments to show characteristics and performance of the pipeline. In addition, a small-scale application for face recognition that makes use of the proposed cleaning process is presented.

A Pipeline to Improve Face Recognition Datasets and Applications

I. Gallo
;
S. Nawaz;A. Calefati;
2018

Abstract

Face recognition has a wide practical applicability in various contexts, for example, detecting students attending a lecture at university, identifying members in a gym or monitoring people in an airport. Recent methods based on Convolutional Neural Network (CNN), such as FaceNet, achieved state-of-the-art performance in face recognition. Inspired from this work, we propose a pipeline to improve face recognition systems based on Center loss. The main advantage is that our approach does not suffer from data expansion as in Triplet loss. Our pipeline is capable of cleaning an existing face dataset to improve the recognition performance or creating one from scratch. We present detailed experiments to show characteristics and performance of the pipeline. In addition, a small-scale application for face recognition that makes use of the proposed cleaning process is presented.
9781728101255
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11383/2083803
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