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Poster
A CASCADED NOISE-ROBUST DEEP CNN FOR FACE RECOGNITION
- Citation Author(s):
- Submitted by:
- Xiangbang Meng
- Last updated:
- 10 September 2019 - 10:52pm
- Document Type:
- Poster
- Document Year:
- 2019
- Event:
- Presenters:
- Xiangbang Meng, Yan Yan,Si Chen, Hanzi Wang
- Categories:
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State-of-the-art face recognition methods have achieved ex- cellent performance on the clean datasets. However, in real- world applications, the captured face images are usually contaminated with noise, which significantly decreases the performance of these face recognition methods. In this pa- per, we propose a cascaded noise-robust deep convolutional neural network (CNR-CNN) method, consisting of two sub- networks, i.e., a denoising sub-network and a face recognition sub-network, for face recognition under noise. Instead of sep- arately training the two sub-networks, we jointly train them in a cascaded manner. As a result, the images generated from the denoising sub-network are beneficial to the training of the face recognition sub-network. Furthermore, the dense connectivity is used to concatenate the feature maps layer- by-layer in the denoising sub-network, which can effectively exploit the shallow and deep features of CNN. Experimental results on public face datasets demonstrate the superior per- formance of the proposed method over several state-of-the-art methods.