| dc.description.abstract | Acquiring the location and orientation of faces from an image sequence is important to driver attention detection, face recognition, and augmented reality applications. In this thesis, we propose a Kalman filter-based method to acquire the face location and orientation from contiguous images. Moreover, we need to transform the non-front-views face to a front-views face based on the acquired orientation data for face recognition application.
There are three steps for face position estimation. The steps include face detection, facial feature points detection, and face position estimation. First, a face is detected based on a circle model. Second, we detect the region of eyes and mouth from face image and then detect the corners from those regions. Finally, the corresponding facial feature points in 3D and 2D coordinates are used to estimate the 3D face pose. To get the accurate 3D coordinates of facial feature points, a 3D face model is constructed. To more accurately detect the 3D face pose, we employ a Kalman filter to detect and track the 3D face pose.
In face recognition, front-view faces are commonly used. A side-view face is generally hard to recognize. In this study, we try to transform the non-front-view faces into the front-view ones for recognition. We hope the transformation may improve the recognition rate. In experiments, we found the accuracy of the proposed pose estimation method is heavily dependent on the parameters and coordinates of the 3D face model. With a set of more accurate parameters and coordinates of the 3D face model. We can get error rate of plus or minus of five degrees or less with estimated rotation angle and known rotation angle for face pose estimation.
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