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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Nooraini Yusoff
UMK City Campus
Computer Science & IT Engineering

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Human identification and classification network based on object hierarchy Loai Alamro; Yuhanis Yusof; Nooraini Yusoff
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 5: October 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i5.pp4412-4423

Abstract

In the real world, objects are categorized hierarchically. However, deep learning (DL)-based computer vision (CV) approaches typically learn to classify objects in a flat manner, which limits their ability to generalize to new objects and scenarios. To address this limitation, a new network called human identification and classification network (HICNet) is proposed for hierarchical-based object identification and classification. HICNet is a hierarchical object identification and classification network with feature extraction layers, an identification branch, and a classification branch. HICNet was evaluated on the labeled faces in the wild (LFW) and labeled faces in the wild-A (LFWA) datasets, where it achieved an identification accuracy of 99.27% and an average classification accuracy of 98.61%. These results demonstrate that HICNet is able to classify objects beyond the training sample(s) by tracing the hierarchy and correlation of the object. HICNet can be used in a variety of CV applications, such as visually impaired aids and robot guidance as it provides a comprehensive insight into the objects.
Co-Authors Loai Alamro Yuhanis Yusof