Regularized Deep Learning for Face Recognition With Weight Variations

Body weight variations are an integral part of a person's aging process. However, the lack of association between the age and the weight of an individual makes it challenging to model these variations for automatic face recognition. In this paper, we propose a regularizer-based approach to lear...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE access Jg. 3; S. 3010 - 3018
Hauptverfasser: Nagpal, Shruti, Singh, Maneet, Singh, Richa, Vatsa, Mayank
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.01.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Body weight
Deep learning
Face recognition
Machine learning
Noise reduction
Regularization
Training data
biometrics
body-weight variations
Face recognition
facial aging
ISSN:2169-3536, 2169-3536
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Body weight variations are an integral part of a person's aging process. However, the lack of association between the age and the weight of an individual makes it challenging to model these variations for automatic face recognition. In this paper, we propose a regularizer-based approach to learn weight invariant facial representations using two different deep learning architectures, namely, sparse-stacked denoising autoencoders and deep Boltzmann machines. We incorporate a body-weight aware regularization parameter in the loss function of these architectures to help learn weight-aware features. The experiments performed on the extended WIT database show that the introduction of weight aware regularization improves the identification accuracy of the architectures both with and without dropout.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2015.2510865