We present an approach to visual tracking based on dividing a target into multiple regions, or fragments. The target is represented by a Gaussian mixture model in a joint feature-spatial space, with each ellipsoid corresponding to a different fragment. The fragments are automatically adapted to the image data, being selected by an efficient region-growing procedure and updated according to a weighted average of the past and present image statistics. Modeling of target and background are performed in a Chan-Vese manner, using the framework of level sets to preserve accurate boundaries of the target. The extracted target boundaries are used to learn the dynamic shape of the target over time, enabling tracking to continue under total occlusion. Experimental results on a number of challenging sequences demonstrate the effectiveness of the technique.

Fingerprint matching algorithm is a key step in fingerprint recognition system. Though there are many existing matching algorithms, there has been inability to match fingerprints in linear time. In this paper we present a novel biometric approach to match fingerprints that run in linear time. We match the minutiae in the fingerprint by constructing a Nearest Neighbor Vector (NNV) considering its k-nearest neighbors. The consolidation of these matched minutiae points is done by incorporating them in binary tree that propagates simultaneously in both fingerprints. This helps our algorithm to run in O(n) time in contrast to many existing algorithms when reference core point is available. We analyze the resulting improvement in computational complexity and present experimental evaluation over FVC2002 database.

We introduce palmprint recognition, one of the most reliable personal identification methods in the biometric technology. In this paper, a new approach to the palmprint matching by constructing local and global line feature structures is presented. The datum point of palmprint acts as an important registration due to its remarkable advantage about its spatial location. Initially we define all possible local line feature structures constructed with adjacent lines, around datum point. Through a first level match using these local line feature structures, we get best matched line features. Using these best matched line features, we construct a global line feature structure of palmprint with datum point as reference. This global line feature structure is spread across in four quadrants, datum point being the origin. We then carry out a second level match using this global structure of palmprint to reliably determine its uniqueness. The two levels of matching using local and global line feature structures helps in effective palmprint recognition. With several palmprint images, we tested out proposed verification system and the experimental result shows that the performance of our algorithm is good.