NDLI: A Deep Structure for Facial Expression Recognition under Partial Occlusion

Content Provider	IEEE Xplore Digital Library
Author	Yue Cheng Bin Jiang Kebin Jia
Copyright Year	2014
Description	Author affiliation: Dept. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China (Yue Cheng; Bin Jiang; Kebin Jia)
Abstract	According to the complex manifestation of human facial expression in realistic environment, occlusion problem has become a new challenge and a hot spot in the field of expression recognition. To make facial expression recognition applied in broader way, the main work is to increase the accuracy under different partial occlusion with feasible robust, which is limited by the information missing and insufficient training with fewer samples. Therefore, an algorithm with a deep structure has been proposed in this paper dealing with four types of frequently occurred occlusion. As a classic method, the Gabor filter is used for feature extraction at first. Then, multi-layers network is used to pre-train the training data samples, with re-describing the input Gabor features in complex way and fine-tuning the weights to refine the learning model. The experimental results on JAFFE database show that the proposed method is valid to achieve better recognition rate especially for partial occlusion on eyes and mouth.
Starting Page	211
Ending Page	214
File Size	563233
Page Count	4
File Format	PDF
e-ISBN	9781479953905
DOI	10.1109/IIH-MSP.2014.59
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-27
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training deep learning Accuracy partial occlusion Face recognition Mouth Feature extraction Gabor filters Face facial expression recognition gabor filter
Content Type	Text
Resource Type	Article

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