A video-based handwritten signature verification framework is proposed in this paper. Using a camera as the sensor has the advantage that the entire writing processes can be captured along with the signatures. The main contribution of this work is that writing postures are analyzed to achieve the verification purpose because the writing postures cannot be easily imitated or forged. The proposed system is able to achieve low false rejection rates while maintaining low false acceptance rates for database containing both unskilled and skilled imitation signatures.
In this work, we propose a method to integrate the Kalman filter and adaptive particle sampling for multiple video object tracking.
The proposed framework is able to detect occlusion and segmentation error cases and perform adaptive particle sampling for accurate
measurement selection. Compared with traditional particle filter based tracking methods, the proposed method generates particles
only when necessary. With the concept of adaptive particle sampling, we can avoid degeneracy problem because the sampling
position and range are dynamically determined by parameters that are updated by Kalman filters. There is no need to spend time on
processing particles with very small weights. The adaptive appearance for the occluded object refers to the prediction results of
Kalman filters to determine the region that should be updated and avoids the problem of using inadequate information to update the
appearance under occlusion cases. The experimental results have shown that a small number of particles are sufficient to achieve high
positioning and scaling accuracy. Also, the employment of adaptive appearance substantially improves the positioning and scaling
accuracy on the tracking results.