Natural Language Access Control Policies (NLACPs) define who can access specific information within an organization and under what conditions. While these policies are typically written in semi-formal or informal natural language, making them easily interpretable by humans, they cannot be directly enforced by access control systems. Their unstructured nature introduces ambiguities and inconsistencies, making automated extraction and translation into structured, machine-enforceable security rules a significant challenge.

A new gait method using the periodic sequence width images and kernel based Fisher discriminant analysis is proposed. The gait pattern is described by the periodic sequence width images. It exacts from the width temporal image generated by calculating the width vector sequences and representing the width value in grey level. The periodic sequence width images capture both the shape structure information of each frame and dynamic properties of gait sequence which represents them in grey level images. This paper use kernel Fisher discriminant analysis to capture and analyze gait features. Kernel Fisher discriminant analysis is based on the Fisher linear discriminant analysis which is optimal for classification and uses the kernel trick. To evaluate the method, we test our method on some common gait database. The result of experiments shows kernel Fisher discriminant analysis can effectively analyze nonlinear gait data and our method is efficient.