Transferred Deep Convolutional Neural Network Features for Extensive Facial Landmark Localization

Abstract

Features are crucial for extensive facial landmark localization (EFLL), while deep convolutional neural network (DCNN) features lead to breakthroughs in diverse visual recognition tasks. However, there is little study of DCNN features for EFLL, mainly because less labeled data with extensive facial landmarks are available. In this letter, we employ transfer learning to overcome this limitation, and utilize DCNN for EFLL. We concentrate the power of DCNN on feature learning within a cascaded-regression framework (CRF). We present three transfer methods, which show the capacity of DCNN as a generic feature extractor, and the benefit of fine-tuning. The proposed specific fine-tuning method for cascaded regression, named cascade transfer, achieves competitive accuracy with state-of-the-art methods on the 300-W challenge dataset.