Open AccessGender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network
Author(s) -
Russel Newlin Shebiah,
Selvaraj Arivazhagan
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12024
Subject(s) - convolutional neural network , discriminative model , computer science , artificial intelligence , context (archaeology) , object (grammar) , flexibility (engineering) , pattern recognition (psychology) , task (project management) , gait , machine learning , artificial neural network , cognitive neuroscience of visual object recognition , computer vision , engineering , mathematics , physiology , paleontology , statistics , systems engineering , biology
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.