PERSON REIDENTIFICATION USING MCE-KISS METRIC LEARNING WITH MAXIMUM LIKELIHOOD FUNCTION - G M, Sree Vrinda; Jayaprakash, Dr. P. (IJRDO -Journal of Computer Science Engineering, 2016)
Nowadays, in the area of Intelligent Video Surveillance (IVR), person reidentification receives an intensive attention. Person reidentification aims to match an instance of a person captured by one camera system to the instance of a person captured by another camera system. It is considered as a challenging problem because the appearance of person varies through the scenes, lightning conditions, shadows, different pose of person that has to be searched for. Recently, many algorithms proposed like LMNN, ITML are not suitable for large training samples. This paper introduces Minimum Classification Error (MCE) based KISS metric algorithm with smoothing technique to improve reidentification. Smoothing technique is done with maximum likelihood functions which enlarge small eigenvalues in the estimated covariance matrices.