Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet  

作　　者：G.Merlin Linda N.V.S.Sree Rathna Lakshmi N.Senthil Murugan Rajendra Prasad Mahapatra V.Muthukumaran M.Sivaram 

机构地区：[1]Department of Computer Science and Engineering,SRM Institute of Science and Technology–Vadapalani Campus,Chennai,India [2]Department of Electronics and Communication Engineering,Agni College of Technology,Chennai,India [3]School of Computer Science and Engineering,VIT-AP University,Amaravati,India [4]Department of Computer Science and Engineering,SRM Institute of Science and Technology,Ghaziabad,India [5]Department of Mathematics,School of Applied Sciences,REVA University,Bangalore,India [6]Faculty of Information Technology,Ton Duc Thang University,Ho Chi Minh City,Vietnam

出　　处：《International Journal of Intelligent Computing and Cybernetics》2022年第3期363-382,共20页智能计算与控制论国际期刊（英文）

摘　　要：Purpose-The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and speech.It proposes a convolutional neural network-based capsule network(CNN-CapsNet)model and outlining the performance of the system in recognition of gait and speech variations.The proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech variations.Design/methodology/approach-This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNNand used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in viewpoint.The proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of CNN-CapsNet.Findings-This research work provides recognition of signal,biometric-based gait recognition and sound/speech analysis.Empirical evaluations are conducted on three aspects of scenarios,namely fixed-view,cross-view and multi-view conditions.The main parameters for recognition of gait are speed,change in clothes,subjects walking with carrying object and intensity of light.Research limitations/implications-The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices.It can also act as a pre-requisite tool to analyze,identify,detect and verify the malware practices.Practical implications-This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor devices.It can also act as a pre-requisite tool to analyze,identify,detect and verify the malware practices.Originality/value-This proposed research work proves to be performing better for the recognition of gait and speech when compared with other techniques.

关 键 词：Intelligent system Gait recognition Convolutional neural network Deep learning Capsule network Viewpoint variations 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]