Continuous Head Pose Estimation Using Manifold Subspace Embedding and Multivariate Regression
نویسندگان
چکیده
منابع مشابه
Person-Independent Head Pose Estimation Using Biased Manifold Embedding
Head pose estimation has been an integral problem in the study of face recognition systems and human-computer interfaces, as part of biometric applications. A fine estimate of the head pose angle is necessary and useful for several face analysis applications. To determine the head pose, face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in hi...
متن کاملAutomatic head pose estimation with Synchronized sub manifold embedding and Random Regression Forests
Head pose can indicate the eye-gaze direction and face toward which is an important part of human motion estimation and understanding. Due to physical factors of the camera, shooting environment, as well as the appearance change of humanity, the head pose estimation becomes a challenging task. Synchronization sub manifold embedding can find the internal structure of nonlinear data for nonlinear...
متن کاملPerson Independent Head Pose Estimation by Non-Linear Regression and Manifold Embedding
This paper describes an approach to head pose estimation in passport type images with an emphasis on high accuracy for near-frontal poses as well as person independence. Two different algorithms are proposed and compared. A Histogram of Oriented Gradients (HOG) descriptor is used for non-linear regression and a Biased Manifold Embedding (BME) approach is extended to cope with multiple poseangle...
متن کاملPose estimation and tracking using multivariate regression
This paper presents an extension of the relevance vector machine (RVM) algorithm to multivariate regression. This allows the application to the task of estimating the pose of an articulated object from a single camera. RVMs are used to learn a oneto-many mapping from image features to state space, thereby being able to handle pose ambiguity.
متن کاملHead Pose Estimation via Manifold Learning
For the last decades, manifold learning has shown its advantage of efficient non-linear dimensionality reduction in data analysis. Based on the assumption that informative and discriminative representation of the data lies on a low-dimensional smooth manifold which implicitly embedded in the original high-dimensional space, manifold learning aims to learn the low-dimensional representation foll...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2018
ISSN: 2169-3536
DOI: 10.1109/access.2018.2817252