Minimum Spanning Tree Based Clustering for Human Gait Classification
- Resource Type
- Conference
- Authors
- Das, S.; Hari Y., P.; Meher, S.; Sahoo, U.K.
- Source
- TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON) Region 10 Conference, TENCON, 2019 - 2019 IEEE. :982-985 Oct, 2019
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Principal component analysis
Kernel
Feature extraction
Gait recognition
Probes
Legged locomotion
Conferences
- Language
- ISSN
- 2159-3450
A gait recognition system usually degrades a lot due to intra-subject variations, such as changing of views, carrying bags during gait sequences. This paper proposes an unsupervised classification algorithm based on diverse viewpoints of gait sequences with respect to normal walk and carrying conditions. This can be achieved with the help of Kernel PCA ($KPCA$) and Minimum Spanning Tree (MST) based clustering. Kernel PCA is a nonlinear form of PCA exploits the spatial structure of gait features. MST based clustering is implemented for classifying different intra subject variations into different clusters. Independent clusters are modeled for different conditions of gait sequences by using successive removal of overlapping nodes, and outliers. Discriminate clusters at the different conditions of training set makes the system more robust for indivisual recognition. A significant EER improvement is achieved using the proposed methods such as (PCA-MST) and (KPCA-MST). To evaluate the performance of the proposed method, experiments are carried out with CASIA dataset to demonstrate the efficacy of the state-of-art techniques.