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Face alignment has been well studied in recent years, however, when a face alignment model is applied on facial images with heavy partial occlusion, the performance deteriorates significantly. In this paper, instead of training an occlusion-aware model with visibility annotation, we address this issue via a model adaptation scheme that uses the result of a(More)
Despite the great success of recent facial landmarks localization approaches, the presence of occlusions significantly degrades the performance of the systems. However, very few works have addressed this problem explicitly due to the high diversity of occlusion in real world. In this paper, we address the face mask reasoning and facial landmarks(More)
Hand detection has many important applications in HCI, yet it is a challenging problem because the appearance of hands can vary greatly in images. In this paper, we propose a novel method for efficient pixel-level hand detection. Unlike previous method which assigns a binary label to every pixel independently, our method estimates a probability shape mask(More)
The problem of face alignment has been intensively studied in the past years. A large number of novel methods have been proposed and reported very good performance on benchmark dataset such as 300W. However, the differences in the experimental setting and evaluation metric, missing details in the description of the methods make it hard to reproduce the(More)
Supervised Descent Method (SDM) has shown good performance in solving non-linear least squares problems in computer vision, giving state of the art results for the problem of face alignment. However, when SDM learns the generic descent maps, it is very difficult to avoid over-fitting due to the high dimensionality of the input features. In this paper we(More)
Face alignment involves locating several facial parts such as eyes, nose and mouth, and has been popularly tackled by fitting deformable models. In this paper, we explore the effect of the combination of structured random regressors and Constrained Local Models (CLMs). Unlike most previous CLMs, we proposed a novel structured random regressors to give a(More)
We propose a two-stage detector that can not only detect and localize hands, but also provide fine-detailed information in the bounding box of hand in an efficient fashion. In the first stage, hand bounding box proposals are generated from a pixel-level hand probability map. Next, each hand proposal is evaluated by a Multi-task Convolutional Neural Network(More)