Affiliations: [a] Department of ECE, Mohamed Sathak Engineering College, Keelakarai, Tamil Nadu, India
| [b] Department of CSE, Syed Ammal Engineering College, Landai, Ramanathapuram, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. M. Amanulla Khan, Assistant professor,
Department of ECE, Mohamed Sathak Engineering College, Keelakarai, Tamil Nadu, India. 623806. E-mail: amanullakhan698@yahoo.com.
Abstract: Gait recognition is the process of recognizing a person based on their walking style. Each person’s walking gait is distinctive and cannot be imitated by others. However, the walking motion of a person will be changed based on their behaviour but their walking pattern doesn’t change. In this paper, a novel Clustering based Faster RCNN has been proposed to identify the single, double and multi-gait. The gait images from the publicly available dataset are pre-processed using Multi scale Retinex (MSR) to reduce the noise artifacts. The Faster RCNN is used for extracting the relevant features from the gait images via the two modules namely CNN and RPN. The CNN layers extract the most relevant features as feature maps and RPN is used for creating the bounding boxes for the extracted features. Fuzzy K-means clustering is used to group the features based on their labels, and it specifies the features acquired using CNN and RPN as input. Finally, the Fast RCNN is employed for classifying the gait images into suspicious and non-suspicious walking pattern. The proposed Clustering based Faster RCNN net achieves the high accuracy rate of 98.74% and 99.19% for suspicious and non-suspicious walking pattern respectively. The proposed Clustering based Faster RCNN model was compared with other traditional models like CNN, U-net, Fab net and Fast R-CNN. The proposed Clustering based Faster RCNN model improves the overall accuracy of 8.86% 33.77% 3.12% and 5.48% better than mmGait, LSTM Net, STDNN and RNN respectively.
Keywords: Gait recognition, deep learning, faster R-CNN, fuzzy K-means clustering, multi scale Retinex
