Here is a round-up of the news articles from this morning. Look at the blog list at the bottom-right for more.
Cnet: "Revamped Google Picasa site identifies photo faces" The "name tag" feature presents users with collections of photos with what it judges to be the same person, then lets them click a button to affix a name. Once photographic subjects are named, users can browse an album of that individual on the fly.
Techcrunch "Picasa Refresh Brings Facial Recognition" The facial recognition technology comes to Picasa thanks to an acquisition Google made in 2006 of Neven Vision, a company that specialized in matching facial detail with images already found in a centralized database. Picasa’a facial recognition technology works in much the same way.
Web Pro News "Googles picasa takes on facial recognition"
It is interesting that Google chose Neven over companies such as
Riya , and
Neven Vision were the creators of the NV1-norm algorithm that did so well in the NIST Face Recogntion Vendors Test .
According to this article Neven have a good patent portfolio in image search. Hartmut Neven was assistant professor of computer science at the University of Southern California at the Laboratory for Biological and Computational Vision. Later he returned as the head of the Laboratory for Human-Machine Interfaces at USC’s Information Sciences Institute. Neven co-founded two companies, Eyematic for which he served as CTO and Neven Vision which he initially led as CEO. At Eyematic he developed real-time facial feature analysis for avatar animation Neven Vision pioneered mobile visual search for camera phones and was acquired by Google in 2006. Today he manages a team responsible for advancing Google’s object and face recognition technologies. I wonder if that means Neven is supervising all the SIFT work for Visual Rank
Detailed List of Neven patents .
The key face recognition patent in this list appears to be US Patent 6,222,939 Granted April 24, 2001 Filed June 25, 1997
Abstract A process for image analysis which includes selecting a number M of images, forming a model graph from each of the number of images, such that each model has a number N of nodes, assembling the model graphs into a gallery, and mapping the gallery of model graphs into an associated bunch graph by using average distance vectors .DELTA..sub.ij for the model graphs as edge vectors in the associated bunch graph. A number M of jets is associated with each node of the associated bunch graph, and at least one jet is labeled with an attribute characteristic of one of the number of images. An elastic graph matching procedure is performed wherein the graph similarity function is replaced by a bunch-similarity function.