In this paper we have developed a robust human identification scheme from low-resolution surveillance video footage. For establishing the human identity; we also carried out a comparative study with two multimodal biometric databases (UCMG and CASIA), two different gait databases with different complexities. For experimental validation of our scheme, we used several dimensionality reduction algorithms (to reduce the dimensionality of the features) and examined number of classifiers to learn the identity model. This study established that gait biometric along with appropriate intelligent processing approaches can allow automatic identity verification from low resolution video surveillance footage.
|Number of pages||12|
|Journal||Journal of Next Generation Information Technology|
|Publication status||Published - 2014|