NDLI: Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network

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Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network

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Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network

Content Provider	IET Digital Library
Author	Russel, Newlin Shebiah Selvaraj, Arivazhagan
Abstract	Age and gender are the two key attributes for healthy social interactions, access control, intelligence marketing etc. Likewise, carried object recognition helps in identifying owner of the baggage being abandoned or the person littering in the public places. The above‐mentioned surveillance task displays discriminative characteristics in gait. Primates can accomplish scene context understanding and reacting to different circumstances with varying reflexes with ease. Human beings achieve this by recollecting prior experiences and adapting to new situations quickly. Modelling the human behaviour, this research work has combined customized and learnable filters so that knowledge database can always be kept up to date, as well as, provides flexibility in learning new contexts. Thus, a specialized parallel deep convolutional neural network architecture with customized filters that extracts intrinsic characteristics and data driven learnable filters are fused to enhance the performance of single convolutional neural network is proposed. From the experimentation it is observed that, the learning is augmented when customized filters and learnable filters are fused together. Results show that the proposed system achieves better performance for CASIA B datAQ2abase and OU‐ISIR gait database‐large population dataset with age and real‐life carried object.
Starting Page	239
Ending Page	251
Page Count	13
Volume Number	15
e-ISSN	17519667
Issue Number	Issue 1, Jan (2021)
Alternate Webpage(s)	https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/ipr2.12024
Alternate Webpage(s)	https://ietresearch.onlinelibrary.wiley.com/toc/17519667/2021/15/1
Journal	IET Image Processing
Publisher Date	2020-11-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Content Type	Text
Resource Type	Article

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