Face recognition among women wearing hijab using deep learning
| dc.contributor.author | Amirul Arif Bin Ab Rasid | |
| dc.date.accessioned | 2021-02-25T02:06:16Z | |
| dc.date.available | 2021-02-25T02:06:16Z | |
| dc.date.issued | 2019-06 | |
| dc.description.abstract | The aim of this project is to create a complete face recognition among ladies who wearing hijab. The hijab conceals hair and other external features of a head (such as the ears). It, therefore, may have implications for the way in which such faces are perceived. With the introduction of Deep Learning (DL), the concept of Convolutional Neural Network (CNN) that was once an idea can be realized. In this project, MATLAB Deep Network Designer is used as the core engine to power the face recognition that aims to help users to identify the faces of women wearing hijab. The users can identify the face even though they wearing hijab. The face recognition also can be performed well in identifying properly and improperly frontalized faces. Optimization is performed by experimenting in stages with several training parameters to obtain the best value for this unique purpose. By using 3 pre-trained CNN models architecture which is AlexNet, GoogleNet, and Vgg16, the best performance of the training algorithm can be produced in order to recognize the face. Combined-algorithm based optimizers play an important role in optimizing the training algorithm. The comparison between the 3 models will be illustrated in this project based on accuracy. The result obtained for AlexNet (61%), GoogleNet (71%), and Vgg16 (79%). | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/11532 | |
| dc.language.iso | en | en_US |
| dc.title | Face recognition among women wearing hijab using deep learning | en_US |
| dc.type | Other | en_US |
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