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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2019

26.10.2018

Fine Tuning Dual Streams Deep Network with Multi-scale Pyramid Decision for Heterogeneous Face Recognition

verfasst von: Weipeng Hu, Haifeng Hu

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

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Abstract

In this paper, we propose a novel method called fine tuning dual streams deep network (FTDSDN) with multi-scale pyramid decision (MsPD) for solving heterogeneous face recognition task. As an extension of classical CNNs, FTDSDN can remove highly non-linear modality information and reserve the discriminative information using Rayleigh quotient objective function. Furthermore, we develop a powerful joint decision strategy called MsPD to adaptively adjust the weight of sub structure and obtain more robust classification performance. Experimental results show our proposed method achieves better performance on the challenging CASIA NIR-VIS 2.0 database, the heterogeneous face biometrics database, the CUHK face sketch FERET database, and the CUHK face sketch database, which demonstrates the effectiveness of our proposed approach.
Vorheriger Artikel Multi-view Opinion Mining with Deep Learning
Nächster Artikel A Parallel Image Skeletonizing Method Using Spiking Neural P Systems with Weights

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Metadaten
Titel
Fine Tuning Dual Streams Deep Network with Multi-scale Pyramid Decision for Heterogeneous Face Recognition
verfasst von
Weipeng Hu
Haifeng Hu
Publikationsdatum
26.10.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9942-1

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