We propose a novel and efficient initialization method for generalized facial landmark localization with an unsupervised roll-angle estimation based on B-spline models. We first show that the roll angle is crucial for an accurate landmark localization. Therefore, we develop an unsupervised roll-angle estimation by adopting a joint 1st -order B-spline model, which is robust to intensity variations and generic for application to various face detectors. The method consists of three steps. First, the scaled-normalized Laplacian of Gaussian operator is applied to a bounding box generated by a face detector for extracting facial feature segments. Second, a joint 1 st -order B-spline model is fitted to the extracted facial feature segments, using an iterative optimization method. Finally, the roll angle is estimated through the aligned segments. We evaluate four state-of-the-art landmark localization schemes with the proposed roll-angle estimation initialization in the benchmark dataset. The proposed method boosts the performance of landmark localization in general, especially for cases with large head pose. Moreover, the proposed unsupervised roll-angle estimation method outperforms the standard supervised methods, such as random forest and support vector regression by 41.6% and 47.2%, respectively.
Discomfort detection for infants is essential in the healthcare domain, since infants lack the ability to verbalize their pain and discomfort. In this paper, we propose a robust and generic discomfort detection for infants by exploiting a novel and efficient initialization method for facial landmark localization, using an unsupervised rollangle estimation. The roll-angle estimation is achieved by fitting a 1st-order B-spline model to facial features obtained from the scaled-normalized Laplacian of the Gaussian operator. The proposed method can be adopted both for daylight and infrared-light images and supports real-time implementation. Experimental results have shown that the proposed method improves the performance of discomfort detection by 6.0% and 4.2% for the AUC and AP using daylight images, together with 6.9% and 3.8% for infrared-light images, respectively.
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