Face Alignment Based on Incremental Learning for Bayonet Surveillance

This paper proposed an improved Local Binary Features (LBF) [1] algorithm for bayonet surveillance system. Since LBF is based on shape-regression strategy, which is prone to over-fitting after multi-stage regression, the training model cannot be directly applied to other scenarios. To this end, we employed new data into existing models at the final stage of regression. As a consequent, newly imported data can be embedded to the model. The experimental results conducted on bayonet surveillance videos showed that the proposed method outperforms LBF, ERT and SDM, concerning with mean errors of the regression results.