Data augmentation for face recognition

Recently, Deep Convolution Neural Networks (DCNNs) have shown outstanding performance in face recognition. However, the supervised training process of DCNN requires a large number of labeled samples which are expensive and time consuming to collect. In this paper, we propose five data augmentation m...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2017-03, Vol.230, p.184-196
Main Authors: Lv, Jiang-Jing, Shao, Xiao-Hu, Huang, Jia-Shui, Zhou, Xiang-Dong, Zhou, Xi
Format: Article
Language:English
Subjects:
3D reconstruction
Data augmentation
Face recognition
Image synthesis
Landmark perturbation
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Summary:Recently, Deep Convolution Neural Networks (DCNNs) have shown outstanding performance in face recognition. However, the supervised training process of DCNN requires a large number of labeled samples which are expensive and time consuming to collect. In this paper, we propose five data augmentation methods dedicated to face images, including landmark perturbation and four synthesis methods (hairstyles, glasses, poses, illuminations). The proposed methods effectively enlarge the training dataset, which alleviates the impacts of misalignment, pose variance, illumination changes and partial occlusions, as well as the overfitting during training. The performance of each data augmentation method is tested on the Multi-PIE database. Furthermore, comparison of these methods are conducted on LFW, YTF and IJB-A databases. Experimental results show that our proposed methods can greatly improve the face recognition performance. •We present five data augmentation methods specific to face images.•Landmark perturbation method is able to generate different kinds of transformed face images automatically.•Different hairstyles and glasses of face image can be automatically synthesized.•Face images with different poses and illuminations can be generated according to 3D face model.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.12.025