dc.description.abstract | This thesis attempts to use deep learning technology combining tracking algorithm to implement a real-time system to recognize a person whose face is partially covered. Although the face is partially covered by a mask, hat or sunglasses, the system can still accurately recognize the face of a person in real time or offline video. To perform facial recognition, we should use the enrollment system to complete the identity enrollment, and the system can recognize the enrolled person immediately without retraining even his/her face is partially covered by something. At the same time, the system will record the time points when the enrolled person enters or leaves the screen. This function is very convenient to be used to search a specific person in a long video.
The partially covered facial recognition has some problems to be overcome. One is if there is no learning about all covered face cases, it is hard to have an accurate recognition result. The other is the covered face should be the exactly front face, otherwise the recognition accuracy will be reduced. Furthermore, some studies used facial repair method to recover the covered part of the face, but it needs a lot of local people’s faces as training data for training. However, the data set of Asian faces is much less than that of European and American faces, which may cause Asian faces having European and American facial features after face repair.
It is known that when people recognize a partially covered face, they usually pay more attention to the uncovered areas of the face. This thesis studies from this point of view and adds the attention mechanism to the network model to improve the backbone architecture of the face recognition network SeesawFaceNet. During the training process, let the covered block be used to augment the training data set. When the network extracts feature of the partially covered faces, it will automatically focus on the uncovered face areas, and strengthen the extraction of the uncovered features to mitigate the impact of feature reduction. Two more contributions of this study are that we do not need a large data set of Asian faces, and can recognize the partially covered faces with different angles. | en_US |