Head Pose Estimation Based On Detecting Facial Features

Abstract

Head pose estimation is recently a more popular area of research. Challenging conditions, such as extreme pose, lighting, and occlusion, has historically hampered traditional, model-based methods. This paper presents a proposal of an integrated method for head pose estimation based on face detection and tracking. This method first locates certain facial features and based on their relative locations determine the head pose, the head pose estimated using coordinates of both eyes and a mouth relative to the nose as the origin of the coordinate system. The nose position is set up as the origin. The coordinates of the other parts defined from the origin, the distance between the face parts normalized so that the coordinates are independent of the image size. For facial feature detection from the detected face region, Haar-like feature utilized along with AdaBoost learning, the Adaboost learning algorithm used for creating optimized learning data. From the experiments, the proposed approach shows robustness in face and facial feature detection and eventually produces better results in estimating head pose rather than simply using Haar-like feature for both face and facial feature detection. The computational cost is low because it uses only those three points.

Head pose estimation through multi-class face segmentation

“…Based on the position of these features neural network estimates three rotation angles i.e., frontal, left and right profile images. Hatem et al [15] method also uses facial features for head pose estimation. Haar like features are used initially for face localization, than the coordinates of eyes and mouth with respect to the nose are located.…”

Anatomical Point-of-Interest Detection in Head MRI Using Multipoint Feature Descriptor

“…The descriptor has neighborhood information on keypoints and is used to identify the same pixel points in various images. A Haarlike feature is a robust point feature description method [34][35][36] and can be used to extract fiducial facial feature points [37,38], the lane line edge [39] and voxel-wise classification in medical image [40]. Fig.…”

Generic and Robust Method for Head Pose Estimation

“…In the literature, the descriptors exploited to estimate the pose, are usually inspired tools used in the analysis of faces or forms, such as: Sobel or Canny filters which are used to extract the facial contours, Gabor filters, nonlinear operator Local Binary Patterns (LBP), the form descriptors (Fourier descriptors, geometrical moments) The color histograms, etc. Among these methods may be mentioned those elaborated in the works: [2][3][4][5][6] and [7]. Otherwise, there are approaches that estimate the pose by aligning a 3D model.…”