Gait based authentication using gait information image features

• The information set that widens the fuzzy set theory.
• A spatiotemporal statistical gait representation, which expresses the statistics of motion patterns.
• The superiority of our features demonstrated by testing them on changed co-variant conditions (i.e. clothing, carrying) and change in speed.
• The performance of the new features is quantified through measures like cumulative match characteristics (CMC).

Human gait, a soft biometric helps to recognize people by the manner, they walk. This paper presents gait image features based on the information set theory, henceforth these are called gait information image features. The information set stems from a fuzzy set with a view to represent the uncertainty in the information source values using the entropy function. The proposed gait information image (GII) is derived by applying the concept of information set on the frames in one gait cycle and two features named gait information image with energy feature (GII-EF) and gait information image with sigmoid feature (GII-SF) are extracted. Nearest neighbor (NN) classifier is applied to identify the gait. The proposed features are tested on Casia-B dataset, SOTON small database with variations in clothing and carrying conditions and on OU-ISIR Treadmill B database with large variation in clothing conditions. Moreover, experiments are carried out on OU-ISIR Treadmill A database with slight variation in the walking speeds to demonstrate the robustness of the proposed features.