Type 2 fuzzy induced person identification using Kinect sensor

Automatic person recognition problem draws significant popularity in the last decade in the field of human-robot interaction. This paper introduces a novel approach to identify a person automatically whom the robot has already met, based on its walking pattern as gait is a unique characteristic for every individual. Here, the Kinect sensor is used to record the gait pattern of a person by storing 20 3-D joint coordinates in each time stamps. The features like joint angle and joint length are obtained from each complete walk cycle. Among all these features, most significant features are selected using principal component analysis. Later, these features are fuzzified constructing a Gaussian membership function with the mean and standard deviation of each feature at different gait cycle. An Interval Type-2 membership is constructed with all these membership values for a particular feature in different trials. 10 walking data set of 10 subjects are processed here. Now, when any person out of these 10 persons is walking in front of Kinect, features are calculated. But as more than one feature value for a particular feature (each feature corresponds to each gait cycle in a complete walking task) is obtained, mean of all these values for a particular feature is considered as measurement point. Defuzzification is done using t-norm and average operators. The person corresponding to highest defuzzified value is considered as the unknown person. The classification accuracy is 89.667%. The proposed method is also compared with few existing person identification techniques and the results obtained prove the superiority of the proposed algorithm.