Head Frontal-View Identification Using Extended LLE
This addresses a challenge in computer vision for applications like face recognition, but it is incremental as it builds on existing LLE techniques.
The paper tackles the problem of automatically identifying frontal head views from multiple facial images with varying yaw poses, using an unsupervised extended LLE method that does not require training samples, and shows reliable identification around the lowest point of the pose manifold, especially for cropped images.
Automatic head frontal-view identification is challenging due to appearance variations caused by pose changes, especially without any training samples. In this paper, we present an unsupervised algorithm for identifying frontal view among multiple facial images under various yaw poses (derived from the same person). Our approach is based on Locally Linear Embedding (LLE), with the assumption that with yaw pose being the only variable, the facial images should lie in a smooth and low dimensional manifold. We horizontally flip the facial images and present two K-nearest neighbor protocols for the original images and the flipped images, respectively. In the proposed extended LLE, for any facial image (original or flipped one), we search (1) the Ko nearest neighbors among the original facial images and (2) the Kf nearest neighbors among the flipped facial images to construct the same neighborhood graph. The extended LLE eliminates the differences (because of background, face position and scale in the whole image and some asymmetry of left-right face) between the original facial image and the flipped facial image at the same yaw pose so that the flipped facial images can be used effectively. Our approach does not need any training samples as prior information. The experimental results show that the frontal view of head can be identified reliably around the lowest point of the pose manifold for multiple facial images, especially the cropped facial images (little background and centered face).