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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shufen Liang Xiangqun Liang Min Guo |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Inf. Eng., Wuyi Univ., Jiangmen, China (Shufen Liang; Xiangqun Liang; Min Guo) |
| Abstract | Most of smile recognition methods are based on constrained databases. Thus there are a lot of limitations when applying those algorithms into the real-world smile recognition. For the purpose of improving the accuracy in real-world smile recognition, we conducted our experiments on two databases (GENKI-4K database and our own built database). Depending on deep learning theory, we constructed a new deep model by stacking Contractive Auto-Encoder (CAE) on Contractive Denoising Auto-Encoder (CDAE) to extract useful features. Firstly, we pre-trained a CDAE to extract the feature of the first layer, then the extracted feature were used as input of the next basic model CAE, by pre-training the CAE model, we got more abstract feature, then the feature were used to classification. Experiments showed that our approach was useful for smile recognition. On the other hand, we also explored the influence of different number of training samples. |
| Starting Page | 176 |
| Ending Page | 181 |
| File Size | 397796 |
| Page Count | 6 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781467376792 |
| DOI | 10.1109/ICNC.2015.7377986 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-08-15 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Databases Error analysis Contractive Auto-Encoder Machine learning Feature extraction Robustness Smile Recogniton Computer aided engineering Contactive Denoising Auto-Encoder Deep Learning Algorithms |
| Content Type | Text |
| Resource Type | Article |
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