Abstract
For robot systems, robust facial landmark detection is the first and critical step for face-based human identification and facial expression recognition. In recent years, the cascaded-regression-based method has achieved excellent performance in facial landmark detection. Nevertheless, it still has certain weakness, such as high sensitivity to the initialization. To address this problem, regression based on multiple initializations is established in a unified model; face shapes are then estimated independently according to these initializations. With a ranking strategy, the best estimate is selected as the final output. Moreover, a face shape model based on restricted Boltzmann machines is built as a constraint to improve the robustness of ranking. Experiments on three challenging datasets demonstrate the effectiveness of the proposed facial landmark detection method against state-of-the-art methods.
Highlights
Facial landmarks encode significant information about face shape deformation
Several methods have been proposed for facial landmark detection, facial point estimation for real-world images with facial expressions, poses, or occlusion has always been a challenging problem, since current approaches struggle to handle the outliers that are generated under these conditions
Wherein I is the image, ÈðSKÀ1; IÞ denotes the histogram of oriented gradient (HOG) features of the local patches, centered at the current landmark locations, and ÁSiK is the difference between the ground truth and the shape estimated in the current iteration
Summary
Facial landmarks encode significant information about face shape deformation. Accurate detection of facial landmark points has aroused great interest, owing to its importance in such applications as human identification, facial animation, and facial expression recognition. RK wherein I is the image, ÈðSKÀ1; IÞ denotes the HOG features of the local patches, centered at the current landmark locations, and ÁSiK is the difference between the ground truth and the shape estimated in the current iteration. To reduce the influence of the initialization, it is chosen to set several different poses as the initializations, instead of using the mean face shape; independent estimation of face landmarks according to these initializations is achieved through cascaded regression The goal of this facial landmark detection algorithm is to determine the best estimates as the final output. Given the training image and the currently estimated landmark locations S, we can calculate the appearance and shape features È and the ground truth score.
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