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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 10/2018

02.03.2017 | Original Article

Multiscale overlapping blocks binarized statistical image features descriptor with flip-free distance for face verification in the wild

verfasst von: Tianyu Geng, Menglong Yang, Zhisheng You, Ying Cai, Feihu Huang

Erschienen in: Neural Computing and Applications | Ausgabe 10/2018

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Abstract

In this work, an effective face verification system based on a fusion of the multiscale overlapping blocks binarized statistical image features (BSIF) descriptor and a flip-free distance is proposed. First, we propose a BSIF with overlapping blocks descriptor and extend it to a multiscale framework. Then, after applying dimensionality reduction, the projected vectors for each scale are scored using two prevalent face verification classifiers: triangular similarity metric learning and the Joint Bayesian method. Moreover, a flip-free distance is applied to boost overall performance. Finally, the different scores for different scales are fused using a support vector machine to further improve performance. We evaluate the proposed face verification system under restricted and unrestricted protocols, for which, in both cases, we achieve very competitive results (90.05 and 93.41%) for the problem of face verification on the Labeled Faces in the Wild dataset.
Vorheriger Artikel The potential of nonparametric model in foundation bearing capacity prediction
Nächster Artikel A study of heat transfer analysis for squeezing flow of a Casson fluid via differential transform method

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Metadaten
Titel
Multiscale overlapping blocks binarized statistical image features descriptor with flip-free distance for face verification in the wild
verfasst von
Tianyu Geng
Menglong Yang
Zhisheng You
Ying Cai
Feihu Huang
Publikationsdatum
02.03.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 10/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2918-7

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