Saidi, Mohamad Hazim (2018) Deep Learning Based Face Attributes Recognition. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted)
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Abstract
Face Recognition is a recently developing technology with numerous real life applications. The goal of this Final Year Project is to create a complete Face Attributes Recognition for security or facility. The automated face identification application is helpful in assisting forensic to survey an area with the implementation of Machine Learning (ML). It was once a difficult challenge due to uncertainties in the captured such as high variation of pose and obstruction corresponding to voluntary and involuntary factors. 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, Fast CNN architecture is used as the core engine to power the face attributes recognition that aims to help users to identify its. The users can identify the face attributes including gender, glasses and facial hair. The face attributes 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. Using this architecture, the best performance of training algorithm can be produced in order to recognize face attributes. Combined-algorithm based optimizers plays an important role in optimizing the training algorithm. The addition of convolutional layer is also essential in order to extract related facial features of facial images.
| Item Type: | Monograph (Project Report) |
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| Subjects: | T Technology T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Monograph |
| Depositing User: | Mr Engku Shahidil Engku Ab Rahman |
| Date Deposited: | 13 Jul 2022 00:38 |
| Last Modified: | 13 Jul 2022 00:38 |
| URI: | http://eprints.usm.my/id/eprint/53327 |
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