A Sift-Point Distribution-Based Method for Head Pose Estimation

Abstract

Estimating the head pose of a person in a video or image sequence is a challenging problem in computer vision. In this project, we present a new technique on how to estimate the human face pose from a video sequence, by creating a probabilistic model based on the scale invariant features of the face. This method consists of four major steps: (1) the face is detected using the basic CAMSHIFT algorithm, (2) a training dataset is created for each face pose, (3) the distinctive invariant features of the training and test face image sets are extracted using the scale-invariant feature transform (SIFT) algorithm, and (4) a kernel density estimate (KDE) of SIFT points on each image is generated. Pose classification is achieved by nearest-neighbor search using a KDE overlap measure. Results indicate that the proposed method is robust, accurate, not computationally expensive, and can successfully be used for pose estimation.