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An improved classifier based on nearest feature line, is proposed in this paper. The new classifier is called as limited nearest feature line (LNFL). The purpose of LNFL is to improve the miss-classification of nearest feature line when the prototypes in NFL are far away from the query sample. A lot of experiments are executed on ORL and AR face database. And the detailed comparison result is given...
In this paper, we propose a very simple face recognition method. This method first exploits a linear combination of all the training samples to express the test sample. Then it evaluates the capability of each class in expressing the test sample and assigns the test sample to the class that has the strongest capability. Using the expression result, the proposed method can classify the testing sample...
The number of video clips available online is growing at a tremendous pace. Conventionally, user-supplied metadata text, such as the title of the video and a set of keywords, has been the only source of indexing information for user-uploaded videos. Automated extraction of video content for unconstrained and large scale video databases is a challenging and yet unsolved problem. In this paper, we present...
3D data registration and classifier are two important components in face recognition system. Aiming at the current methods' handicaps such as slow convergence and easiness of getting into local optimization, this paper presents a novel face recognition method using filled function, one of the effective deterministic methods. It gives a modified concept of filled function based on Ge, and proposes...
The phenomenal growth of video on the Web and the increasing sparseness of meta information associated with it forces us to look for signals from the video content for search/information retrieval and browsing based corpus exploration. A large chunk of users' searching/browsing patterns are centered around people present in the video. Doing it at scale in videos remains hard due to a) the absence...
A typical automatic face recognition system is composed of three parts: face detection, face alignment and face recognition. Conventionally, these three parts are processed in a bottom-up manner: face detection is performed first, then the results are passed to face alignment, and finally to face recognition. The bottom-up approach is one extreme of vision approaches. The other extreme approach is...
Active statistical models including active shape models and active appearance models are very powerful for face alignment. They are composed of two parts: the subspace model(s) and the search process. While these two parts are closely correlated, existing efforts treated them separately and had not considered how to optimize them overall. Another problem with the subspace model(s) is that the two...
Efficient 3D face reconstruction is very important for face animation and recognition. The slow speed of the 3D morphable model is due to the texture mapping. To improve the speed, we only use the shape matching to recover the 3D shape and use texture mapping to get the texture. However, only with the shape information, one image is not enough for accurate 3D face reconstruction. So, we propose to...
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