A robust similarity based deep siamese convolutional neural network for gait recognition across views

Abstract

Gait recognition has been considered as the emerging biometric technology for identifying the walking behaviors of humans. The major challenges addressed in this article is significant variation caused by covariate factors such as clothing, carrying conditions and view angle variations will undesirably affect the recognition performance of gait. In recent years, deep learning technique has produced a phenomenal performance accuracy on various challenging problems based on classification. Due to an enormous amount of data in the real world, convolutional neural network will approximate complex nonlinear functions in models to develop a generalized deep convolutional neural network (DCNN) architecture for gait recognition. DCNN can handle relatively large multiview datasets with or without using any data augmentation and fine‐tuning techniques. This article proposes a color‐mapped contour gait image as gait feature for addressing the variations caused by the cofactors and gait recognition across views. We have also compared the various edge detection algorithms for gait template generation and chosen the best from among them. The databases considered for our work includes the most widely used CASIA‐B dataset and OULP database. Our experiments show significant improvement in the gait recognition for fixed‐view, crossview, and multiview compared with the recent methodologies.

Accurate metaheuristic deep convolutional structure for a robust human gait recognition

“…Generally, the GWO prediction process is divided into three stages: surrounding, hunting, and attacking. When the grey wolves sense the presence of prey, they round it using the (1) to (4).…”

“…Their experimental results scored 90.5% and 91.3% of accuracy values on CASIA-B and OU-ISIR datasets, respectively. George et al [4] presented a color-mapped contour gait image as a gait characteristic for tackling cofactor variations and the identification of gait across perspectives. Moreover, they compared and chose the best edge detection methods for gait blueprint generation on the CASIA-B dataset and the OULP database.…”

Biometric-Based Security System for Smart Riding Clubs

“…Song et al [15], Zhang et al [77] and Merlin et al [37] are among the works based on Siamese architectures for gait classification and matching: Song et al [15] proposed a network known as GaitNet which is composed of two convolutional neural networks. One corresponds to gait segmentation and the other corresponds to classification.…”

“…Merlin et al [37] proposed the CCGI gait feature which keeps more temporal and spatial differences in the gait patterns. the used model based on a convolutional neural network (CNN) gave an empirical evaluation to recognize and classify the discriminative changes of the CCGI feature.…”

Interpretation of Expressions through Hand Signs Using Deep Learning Techniques

“…Another convolutional architecture, LeNet-05 [25] is proposed by LeCun, initially for handwritten character recognition. This CNN-based model is capable enough of taking multiple objects as input and producing multiple outputs in a single pass without prior segmentation, which is called Space Displacement Neural Network (SDCNN) [26]. Sparse Convolutional layers and max-pooling layers are the souls of LeNet architecture.…”