Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

The demand for a non-contact biometric approach for candidate identification has grown over the past ten years.Based on the most important biometric application,human gait analysis is a significant research topic in computer vision.Researchers have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far away.Gait recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access control.These systems require a complex combination of technical,operational,and definitional considerations.The employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked applications.Thiswork proposes a novel deep learning-based framework for human gait classification in video sequences.This framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing clothes.The proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer learning.Next,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)algorithm.Then,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm.This algorithm chooses the best features,combined in a novel correlation-based fusion technique.Finally,the fused best features are categorized using medium,bi-layer,and tri-layered neural networks.On the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was achieved.The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work.

Compensation Algorithm Based on Estimation State for Transmission Delay in Cooperative Localization

In order to maximize the utilization of the observation information in the cooperative localization,a compensation algorithm based on the estimation state is presented for transmission delay.Under the framework of the Kalman filter,two different processes of state estimating with and without transmission delay are investigated and contrasted.The expression of difference quantity caused by transmission delay is derived.It is used to compensate the present estimation state instead of the observed information compensation.According to the characteristics of state transition matrix,an equivalent expression of which successively impacts on the covariance factor in delay time is obtained.The simulation results show that the present estimated state is effectively corrected by transmission information and the relevance among agents is accurately updated.As a result,a higher positioning accuracy is achieved.Meanwhile,the consumption of recording and multiplication of the state transition matrix is saved.