Loading [a11y]/accessibility-menu.js
A Light CNN for Deep Face Representation With Noisy Labels | IEEE Journals & Magazine | IEEE Xplore

A Light CNN for Deep Face Representation With Noisy Labels


Abstract:

The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit the large amount of training data...Show More

Abstract:

The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit the large amount of training data. When training data are obtained from the Internet, the labels are likely to be ambiguous and inaccurate. This paper presents a Light CNN framework to learn a compact embedding on the large-scale face data with massive noisy labels. First, we introduce a variation of maxout activation, called max-feature-map (MFM), into each convolutional layer of CNN. Different from maxout activation that uses many feature maps to linearly approximate an arbitrary convex activation function, MFM does so via a competitive relationship. MFM can not only separate noisy and informative signals but also play the role of feature selection between two feature maps. Second, three networks are carefully designed to obtain better performance, meanwhile, reducing the number of parameters and computational costs. Finally, a semantic bootstrapping method is proposed to make the prediction of the networks more consistent with noisy labels. Experimental results show that the proposed framework can utilize large-scale noisy data to learn a Light model that is efficient in computational costs and storage spaces. The learned single network with a 256-D representation achieves state-of-the-art results on various face benchmarks without fine-tuning.
Published in: IEEE Transactions on Information Forensics and Security ( Volume: 13, Issue: 11, November 2018)
Page(s): 2884 - 2896
Date of Publication: 03 May 2018

ISSN Information:

Funding Agency:


References

References is not available for this document.