Associated professor | Menoufia University | EgyptÂ
Assoc. Prof. Dr. Ibrahim Omara is a dedicated researcher specializing in Cybersecurity, Artificial Intelligence, Machine Learning, Computer Vision, Multi-Biometrics, and Image Classification, with a growing influence across these interconnected domains. His scholarly contributions include 25 research documents, which have collectively earned 413 citations, supported by an h-index of 11 and i10-index of 12, highlighting both productivity and consistent scholarly impact. His work is highly recognized within the biometric research community, particularly for advancing ear recognition, multimodal biometric fusion, and deep feature learning, where several of his publications have become widely cited references.A significant portion of his contributions lies in pioneering geometric feature extraction, Mahalanobis distance learning, pairwise SVM classification, and distance-metric-driven multimodal authentication, including models that integrate deep CNNs, Vision Transformers, and feature-level fusion. His article A novel geometric feature extraction method for ear recognition stands among his most influential works, shaping subsequent research directions within biometric pattern recognition. In addition to ear biometrics, he has also contributed to remote sensing, SAR target classification, hyperspectral imagery transmission, and deep reinforcement learning, reflecting a multidisciplinary research approach.He has collaborated extensively with leading international researchers, including experts from Harbin Institute of Technology, Dublin City University, Nanyang Technological University, Benha University, Menoufia University, and Prince Sultan University. These collaborations have strengthened cross-institutional innovation in AI-driven security systems, robust biometrics, and intelligent vision technologies. His research outputs also include recent advancements in multi-biometric models, finger-knuckle recognition, and high-resolution scene classification, demonstrating continuous engagement with state-of-the-art machine intelligence.The social impact of his work is reflected in applications that enhance secure identification, digital authentication, and automated visual intelligence, contributing to safer digital ecosystems and improved trust in AI-enabled technologies. With a strong publication record and sustained research momentum, he remains committed to advancing next-generation intelligent security systems and expanding the frontiers of biometric artificial intelligence.
Featured Publications
1. Omara, I., Li, F., Zhang, H., & Zuo, W. (2016). A novel geometric feature extraction method for ear recognition. Expert Systems with Applications, 65, 127–135. Cited By : 100
2.Omara, I., Wu, X., Zhang, H., Du, Y., & Zuo, W. (2018). Learning pairwise SVM on hierarchical deep features for ear recognition. IET Biometrics, 7(6), 557–566. Cited By : 43
3.Omara, I., Hagag, A., Chaib, S., Ma, G., Abd El-Samie, F. E., & Song, E. (2020). A hybrid model combining learning distance metric and DAG support vector machine for multimodal biometric recognition. IEEE Access.
Cited By : 36
4.Omara, I., Wu, X., Zhang, H., Du, Y., & Zuo, W. (2017). Learning pairwise SVM on deep features for ear recognition. In Proceedings of the 2017 IEEE/ACIS 16th International Conference on Computer and Information. Cited By : 36
5.Omara, I., Hagag, A., Ma, G., Abd El-Samie, F. E., & Song, E. (2021). A novel approach for ear recognition: Learning Mahalanobis distance features from deep CNNs. Machine Vision and Applications, 32(1), 38. Cited By : 35
His contributions in AI-driven biometrics and intelligent security models provide industry with scalable, high-accuracy authentication solutions. This research accelerates technological innovation, enhances digital infrastructure reliability, and supports global transitions toward secure, intelligent, and automated systems.
