Person identification from full-body movement using string grammar fuzzy-possibilistic C-medians

• Main Authors: Watchanan Chantapakul, Sansanee Auephanwiriyakul, Nipon Theera-Umpon, Navadon Khunlertgit
• Format: Conference Proceeding
• Published: 2019
• Subjects: Chemical Engineering; Computer Science; Engineering; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85065015091&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/65460
• Institution: Chiang Mai University

© 2018 IEEE. Biometrics is a method to identify a person. However, there are several biometric techniques including face recognition, palm recognition, iris recognition, fingerprint recognition, and so on. There is a new approach in biometrics, i.e., identification using full-body movement. In this paper, we introduce a full-body movement in human identification using three Kinects. In particular, we utilize the string grammar fuzzy-possibilistic C-medians (sgFPCMed) to group string sequences from skeleton frames into pose string, then group the pose string sequences of each person into multi-group to create multi-prototypes for each person. The K-nearest neighbor is used to identify the person in the test process on 27 subjects. The system yields 73.33% correct classification on the best validation set of four-fold cross validation.