Towards Unsupervised Face Recognition in Surveillance Video: Learning with Less Labels
To tackle re-identify people within different operation surveillance cameras using the existing state-of-the art supervised approaches, we need massive amount of annotated data for training. Training model with less human annotations is a though task while of great significance, especially in deploying new recognition algorithms to a large-scale real-work surveillance camera networks. This project aims to develop reliable unsupervised method for handling the situation when large number of labeled data is not available in real world applications. We tried to combine unsupervised deep learning approach with new graph regularization to incrementally improve the recognition accuracy.
Pei Li
