Face recognition for video surveillance with aligned facial landmarks learning

BACKGROUND: Video-based face recognition has attracted much attention owning to its wide range of applications such as video surveillance. There are various approaches for facial feature extraction. Feature vectors extracted by these approaches tend to have large dimension and may include redundant information for face representation, which limits the application of methods with high accuracy such as machine learning. OBJECTIVE: Facial landmarks represent the intrinsic characteristics of human face, which can be utilized to decrease redundant information and reduce the computation complexity. But feature points extracted in each frame of a video are irregular which needed to be aligned. METHODS: This paper presents a novel method which is based on facial landmarks and machine learning. We proposed a method to align the feature data into a common co-ordinate frame, and use a robust AdaBoost algorithm for classification. RESULTS: Experiments on the public Honda/UCSD database demonstrate the superior performance of our method to several state-of-the-art approaches. Experiments on Yale database show the sensitivity and specificity of the proposed method. CONCLUSION: The proposed methods can improve the image-set based recognition performance.