Unsupervised face analysis from multi-view - Seyed Mohammad Hassan Anvar (2014)
Face detection, localization and recognition from multi-poses are one of the most challenging topics in the area of computer vision and pattern recognition. During the past decade several methods have been proposed for face detection and recognition but they mainly concentrate on frontal faces. Although in recent years, some methods have been proposed based on sliding windows to exhaustively search through the image in many scales and views, they are slow and their performance on multi-view or multi-pose faces are not as good as frontal face detections. Training these methods is also a bottleneck. Most of the proposed methods require thousands of positive and negative samples from different face poses for training. These images for training have to be manually cropped, aligned and labeled, thus is manually intensive and expensive to perform. Note that since the effect of variation in the face pose captured on a fix camera and change in camera view but with a fixed face pose is the same, we used both words interchangeably,