Iris recognition based on robust principal component analysis
Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.
In estimating the state of thrusting/ballistic endoatmospheric projectiles for the end purpose of impact point ::: prediction (IPP), the total observation time, the wind effect and the sensor accuracy significantly affect the IPP ::: performance. First the tracker accounting for the wind effect is presented. Following this, based on the multiple ::: interacting multiple model (MIMM) estimator developed recently, a sensitivity study of the IPP performance with ::: respect to the total observation time, the wind (strength and direction) and the sensor accuracy is presented.