In this paper, we propose a novel face alignment method that trains deep convolutional network from coarse to fine. It divides given landmarks into principal subset and elaborate subset. We firstly keep a large weight for principal subset to make our network primarily predict their locations while slightly take elaborate subset into account. Next the weight of principal subset is gradually decreased until two subsets have equivalent weights. This process contributes to learn a good initial model and search the optimal model smoothly to avoid missing fairly good intermediate models in subsequent procedures. On the challenging COFW dataset [1], our method achieves 6.33 mean error with a reduction of 21.37 compared with the best previous result [2].
@cite_13 trains a deep convolutional network with multitask learning which jointly optimizes landmark detection together with the recognition of some facial attributes. It pre-trains the network by five landmarks and then fine-tunes to predict the dense landmarks. However, our method doesn't require labeling extra attributes for training samples. Different from pre-training, we also consider predicting the location of other elaborate landmarks. Compared to the method consists of pre-training and fine-tuning, we gradually adjust the weight of principal subset and elaborate subset respectively to avoid missing good models in subsequent training procedures.
@cite_13
1795776638
In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with the recognition of heterogeneous but subtly correlated facial attributes, such as gender, expression, and appearance attributes. This is non-trivial since different attribute inference tasks have different learning difficulties and convergence rates. To address this problem, we formulate a novel tasks-constrained deep model, which not only learns the inter-task correlation but also employs dynamic task coefficients to facilitate the optimization convergence when learning multiple complex tasks. Extensive evaluations show that the proposed task-constrained learning (i) outperforms existing face alignment methods, especially in dealing with faces with severe occlusion and pose variation, and (ii) reduces model complexity drastically compared to the state-of-the-art methods based on cascaded deep model.