VGR-net: A view invariant gait recognition network

Abstract

Biometric identification systems have become immensely popular and important because of their high reliability and efficiency. However person identification at a distance, still remains a challenging problem. Gait can be seen as an essential biometric feature for human recognition and identification. It can be easily acquired from a distance and does not require any user cooperation thus making it suitable for surveillance. But the task of recognizing an individual using gait can be adversely affected by varying view points making this task more and more challenging. Our proposed approach tackles this problem by identifying spatio-temporal features and performing extensive experimentation and training mechanism. In this paper, we propose a 3-D Convolution Deep Neural Network for person identification using gait under multiple view. It is a 2-stage network, in which we have a classification network that initially identifies the viewing point angle. After that another set of networks (one for each angle) has been trained to identify the person under a particular viewing angle. We have tested this network over CASIA-B publicly available database and have achieved state-of-the-art results. The proposed system is much more efficient in terms of time and space and performing better for almost all angles.

Video Based Shuffling Step Detection for Parkinsonian Patients Using 3D Convolution

“…To solve the problem that convolutional network couldn't deal with long image sequences, a gait sequence was cut into several short sequences as the input of the network. Thapar et al [18] proposed a two-stage method to identify human gait from multiple views. A 3D convolutional neural network was designed to estimate the viewing angle and perform subject identification.…”

Gait Recognition via Cross Walking Condition Constraint

“…Reference [15] uses LSTM and CNN to extract spatial and temporal gait features. Reference [16] apply 3D convolution operation on feature maps of frames. GaitNet [17] disentangles gait features from colored images via novel losses and uses LSTM to extract temporal gait information.…”

“…Overall Loss: The overall loss includes hard triplet loss, identification loss, XCenter loss and XPair loss. The equation of overall loss function can be expressed as: (16) where λ xcen and λ xpair control the importance of XCenter loss and XPair loss, respectively.…”

“…Batch size can be denoted as (p, k), where p represents the number of subjects, and k represents the number of samples selected from each subject. The batch size of experiment implemented on CASIA-B is (4,16). We train our model for 15K iterations, which is notable that our model converges significantly faster than previous state-of-the-art models [11,12] during training.…”

Review of gait recognition approaches and their challenges on view changes

“…The two‐stage 3D CNN is used in [103]. The first stage is to construct the 3D network for classifying the viewing angle.…”