Face recognition has been actively studied over the past decade and continues to be a big research challenge. Just recently, researchers have begun to investigate face recognition under unconstrained conditions. Unconstrained Face Recognition provides a comprehensive review of this biometric, especially face recognition from video, assembling a collection of novel approaches that are able to recognize human faces under various unconstrained situations. The underlying basis of these approaches is that, unlike conventional face recognition algorithms, they exploit the inherent characteristics of the unconstrained situation and thus improve the recognition performance when compared with conventional algorithms. Unconstrained Face Recognition is structured to meet the needs of a professional audience of researchers and practitioners in industry. This volume is also suitable for advanced-level students in computer science.The testing strategy is similar to that described in Chapter 4. l Learn W from the training set using the bilinear leaming algorithm [2 14, 95]. ... learn the identity signature f a#39;s (as well as sa#39;s) for all gallery and probe elements (an element is an image in Scenario A and a group of images in Scenario B) using the ... Learning f and s from one single image takes about l-2 seconds in a Matlab implementation.
|Title||:||Unconstrained Face Recognition|
|Author||:||Shaohua Kevin Zhou, Ramalingam Chellappa, Wenyi Zhao|
|Publisher||:||Springer Science & Business Media - 2006-10-11|