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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 2/2018

09.02.2018

Improving Deep Learning Feature with Facial Texture Feature for Face Recognition

verfasst von: Yunfei Li, Zhaoyang Lu, Jing Li, Yanzi Deng

Erschienen in: Wireless Personal Communications | Ausgabe 2/2018

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Abstract

Face recognition in the reality, is a challenging problem, due to varieties in illumination, background, pose etc. Recently, the deep learning based face recognition algorithm is able to learn effective face features to obtain a very impressive performance. However, this kind of face recognition algorithm completely relies on the machine learning based face features, while ignores the useful experience in hand-craft features which have been studied in a long period. Therefore, a face recognition based on facial texture feature aided deep learning feature (FTFA-DLF) is proposed in this paper. The proposed FTFA-DLF is able to combine the benefits of deep learning and hand-craft features. In the proposed FTFA-DLF method, the hand-craft features are texture features extracted from the eyes, nose, and mouth regions. Then, the hand-craft features are used to aid deep learning features by adding both deep learning and hand-craft features into the objective function layer, which adaptively adjusts the deep learning features so that it can better cooperate with the hand-craft features and obtain a better face recognition performance. Experimental results show that the proposed face recognition algorithm on the LFW face database to achieve the accuracy rate of 97.02%.
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Metadaten
Titel
Improving Deep Learning Feature with Facial Texture Feature for Face Recognition
verfasst von
Yunfei Li
Zhaoyang Lu
Jing Li
Yanzi Deng
Publikationsdatum
09.02.2018
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 2/2018
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-018-5377-2

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