Automatic head pose estimation with Synchronized sub manifold embedding and Random Regression Forests

نویسندگان

  • Yulian Zhu
  • Zhimei Xue
  • Chunyan Li
چکیده

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 dimensionality reduction and random regression forests can make the nonlinear function mapping for getting the right head pose. In this paper, the advantages of these two algorithms are combined with a method for solving the head pose estimation. Data collection step, the depth data come from the 3D sensor; and training data step, the data is using the local linear structure for label and using a statistical model for synchronization pose samples. Meanwhile the experimental results on a publicly available database prove that the proposed algorithm can achieve state-of-the-art performance while the current estimate has a faster speed and higher robustness when large range of pose changes and outperforms existing.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Real-Time Head Pose Estimation Using Random Regression Forests

Automatic head pose estimation is useful in human computer interaction and biometric recognition. However, it is a very challenging problem. To achieve robust for head pose estimation, a novel method based on depth images is proposed in this paper. The bilateral symmetry of face is utilized to design a discriminative integral slice feature, which is presented as a 3D vector from the geometric c...

متن کامل

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 کامل

Continous Head Pose Estimation using Random Regression Forests

Head pose is a rich visual cue that finds great interest in the field of human robot interaction (HRI) and for video surveillance applications. Previous attempts at solving this problem have often proposed solutions formulated in a classification setting. Furthermore, strong assumptions on illumination and scale in an occlusion-free environment have usually been made. We propose a regression so...

متن کامل

Person 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 کامل

Real Time Head Pose Estimation from Consumer Depth Cameras

We present a system for estimating location and orientation of a person’s head, from depth data acquired by a low quality device. Our approach is based on discriminative random regression forests: ensembles of random trees trained by splitting each node so as to simultaneously reduce the entropy of the class labels distribution and the variance of the head position and orientation. We evaluate ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2014