Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition
Joint Authors
Huang, Rongbing
Liu, Chang
Li, Guoqi
Zhou, Jiliu
Source
Mathematical Problems in Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-19
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Based on a special type of denoising autoencoder (DAE) and image reconstruction, we present a novel supervised deep learning framework for face recognition (FR).
Unlike existing deep autoencoder which is unsupervised face recognition method, the proposed method takes class label information from training samples into account in the deep learning procedure and can automatically discover the underlying nonlinear manifold structures.
Specifically, we define an Adaptive Deep Supervised Network Template (ADSNT) with the supervised autoencoder which is trained to extract characteristic features from corrupted/clean facial images and reconstruct the corresponding similar facial images.
The reconstruction is realized by a so-called “bottleneck” neural network that learns to map face images into a low-dimensional vector and reconstruct the respective corresponding face images from the mapping vectors.
Having trained the ADSNT, a new face image can then be recognized by comparing its reconstruction image with individual gallery images, respectively.
Extensive experiments on three databases including AR, PubFig, and Extended Yale B demonstrate that the proposed method can significantly improve the accuracy of face recognition under enormous illumination, pose change, and a fraction of occlusion.
American Psychological Association (APA)
Huang, Rongbing& Liu, Chang& Li, Guoqi& Zhou, Jiliu. 2016. Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1112493
Modern Language Association (MLA)
Huang, Rongbing…[et al.]. Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition. Mathematical Problems in Engineering No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1112493
American Medical Association (AMA)
Huang, Rongbing& Liu, Chang& Li, Guoqi& Zhou, Jiliu. Adaptive Deep Supervised Autoencoder Based Image Reconstruction for Face Recognition. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1112493
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1112493