| Proj No. | A3010-251 |
| Title | Autoencoder for Face Recognition |
| Summary | This project studies deep learning-based autoencoder for face recognition using a convolutional neural network. Autoencoders find a low-dimensional set of features representing a face. The performance of an autoencoder will be compared against a high-dimensional full-input CNN for face recognition. Some face recognition applications are constrained by bandwidth and/or computation, and using low-dimensional features will be useful. Adequate data and training are necessary to enhance the autoencoder's capabilities. Python or any other language may be used for coding. |
| Supervisor | A/P Anamitra Makur (Loc:S1 > S1 B1C > S1 B1C 103, Ext: +65 67904013) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Machine Learning and Data Analytics Lab (Loc: S2.1, B4-01) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |