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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences
Adil Abdulhur Abushana
University of Kufa
Computer Science & IT Earth & Planetary Sciences Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Robust multi-faces recognition and tracking via fuzzy genetic algorithms and deep coupled features Adil Abdulhur Abushana; Yousif Samer Mudhafar
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp208-217

Abstract

In real-world surveillance environments, face recognition and tracking remain challenging due to partial occlusion, pose variation, illumination changes, and background clutter. This paper presents a robust hybrid framework that integrates fuzzy genetic algorithms (FGA) with deep coupled feature learning for multi-face recognition and tracking. The proposed system comprises three main modules: i) face detection and pre processing using the multi-task cascaded convolutional network (MTCNN), ii) deep coupled ResNet embeddings that jointly learn identity and appearance-invariant representations, and iii) a fuzzy rule-based genetic optimizer that adaptively refines tracking decisions based on uncertainty in motion, appearance similarity, and occlusion levels. The novelty of this work lies in the fusion of fuzzy inference with evolutionary search to guide the genetic optimization process—allowing dynamic adaptation to noisy and uncertain visual conditions. Moreover, probabilistic data association filters (PDAF) and conditional joint likelihood filters (CJLF) are employed to further enhance temporal consistency under occlusion and appearance variation. The results confirm that fuzzy evolutionary optimization, when coupled with deep feature learning, significantly improves robustness and stability for real-time face tracking in complex, dynamic scenes.
Dynamic sports environments: automatic feature players tracking and re-identification with similar appearances Adil Abdulhur Abushana; Riyadh Alsaeedi
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.pp4292-4303

Abstract

Tracking multiple players in sports videos remains challenging due to frequent occlusions, similar appearances among players, and rapid motion. This paper presents a comprehensive player tracking framework that integrates deep player re-identification (DPRI), an individual probabilistic overlapping bounding box occupancy mapping (IPOM) for robust spatial localization under severe overlap, and an identity-aware k-shortest path optimization algorithm (KSP-ID) for improved trajectory association. The DPRI module combines pose-based embeddings, jersey appearance cues, and facial features to generate discriminative identity representations. Experimental evaluation on the autonomous production of images based on distributed and intelligent sensing (APIDIS) and Shantou University (STU) datasets demonstrates strong performance, achieving 85.5% multiple object tracking accuracy (MOTA), 69.0% generalized multiple object tracking accuracy (GMOTA), and 90.2% rank-1 re-identification accuracy. Furthermore, the proposed framework significantly reduces identity switches (IDS) compared with conventional k-shortest path (KSP) approaches, probabilistic occupancy models, and recent deep learning-based tracking methods. These results demonstrate that integrating semantic identity information with spatial reasoning enables robust and efficient player tracking in highly dynamic sports environments.
Co-Authors Riyadh Alsaeedi Yousif Samer Mudhafar