Dr. Rachid Aliradi is an accomplished researcher and academic with extensive expertise in computer vision, pattern recognition, biometrics, and machine and deep learning. Over a career spanning more than 20 years, Dr. Aliradi has significantly contributed to both foundational research and applied projects in image and multimedia analysis, 2D and 3D face recognition, kinship verification, crowd analysis, gesture analysis, and the development of efficient tensor analysis methods.Dr. Aliradi has held key academic and research positions, including Associate Professor at CERIST, Algeria (2011–2025), and Visiting Researcher roles at the University of Montreal, Canada (2015), and the University of Louisville, USA (2017–2018). He also served as a PhD Assistant Professor and contributed to the instruction and development of advanced courses ranging from algorithmics and object-oriented programming to digital communication and image analysis across undergraduate and graduate levels.His research portfolio includes numerous high-impact projects such as facial recognition for early cancer detection, multimedia security, biometric systems, and crowd behavior analysis, as well as ongoing work in optimizing cardiac diagnostics. Dr. Aliradi has collaborated with international researchers, including Prof. Abdelmalik Ouamane, Prof. Adel Elmaghraby, and Dr. Youssef Amghar, among others, reflecting a strong record of global academic partnerships.Dr. Aliradi has authored over 50 publications, with 84 citations and an h-index of 5, in leading journals and conferences including Multimedia Tools and Applications, IEEE/WIC/ACM International Conferences, and Preprints.org. Notable contributions include the development of DIEDA for face and kinship verification, TXQEDA tensor-based methods for facial pain detection, and novel descriptors for image analysis and semantic multimedia indexing.Beyond research, Dr. Aliradi’s work has had tangible societal impact, particularly in healthcare and security, by advancing early disease detection, improving biometrics, and enhancing automated image-based analysis systems. His ongoing dedication to scientific innovation, education, and cross-border collaboration positions him as a leading expert in the fields of computer vision and intelligent systems.
Featured Publications
1.Aliradi, R., Belkhir, A., Ouamane, A., & Elmaghraby, A. S. (2018). DIEDA: Discriminative information based on exponential discriminant analysis combined with local features representation for face and kinship verification. Multimedia Tools and Applications, 1–18. Cited By : 23
2.Aliradi, R., Bouzera, N., Meziane, A., & Belkhir, A. (2013). Detection of facial components based on SVM classification and invariant feature. In 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (pp. xx–xx). IEEE.Cited By : 16
3.Aliradi, R., & Ouamane, A. (2024). A novel descriptor (LGBQ) based on Gabor filters. Multimedia Tools and Applications, 83(4), 11669–11686. Cited By : 12
4.Amrane, A., Mellah, H., Aliradi, R., & Amghar, Y. (2014). Semantic indexing of multimedia content using textual and visual information. International Journal of Advanced Media and Communication, 5(2–3), 182–194. Cited By : 10
5.Aliradi, R., Belkhir, A., Ouamane, A., Aliane, H., Sellam, A., & Amrane, A. (2018). Face and kinship image based on combination descriptors-DIEDA for large scale features. In 21st Saudi Computer Society National Computer Conference (NCC) (pp. 1–6).Cited By : 7
Dr. Aliradi’s research advances intelligent image and video analysis, enabling breakthroughs in biometrics, healthcare, and multimedia security. His innovative methods for facial recognition, kinship verification, and crowd analysis directly enhance societal safety, early disease detection, and global technological innovation