N. Siddique

Work place: School of Computing, Engineering and Intelligent Systems, Ulster University, United Kingdom

E-mail: nh.siddique@ulster.ac.uk


Research Interests: Computing Platform, Robotics, Artificial Intelligence


N. Siddique is with the School of Computing, Engineering and Intelligent Systems, Ulster University. He obtained Dipl.-Ing. degree in Cybernetics from the TU Dresden, Germany, MSc in Computer Science from BUET, Bangladesh and PhD in Intelligent Control from the Department of Automatic Control and Systems Engineering, University of Sheffield, England. His research interests include: robotics, cybernetics, computational intelligence, nature-inspired computing, stochastic systems and vehicular communication. He has published over 170 research papers. He authored and co-authored five books published by John Wiley, Springer and Taylor & Francis. He guest edited eight special issues of reputed journals on Cybernetic Intelligence, Computational Intelligence, Neural Networks and Robotics. He has been involved in organizing many international conferences and co-edited conference proceedings. He is a Fellow of the Higher Education Academy, a senior member of IEEE and a member of different committees of IEEE SMCS. He is on the editorial board of the Nature Scientific Research, Journal of Behavioural Robotics, Engineering Letters, International Journal of Machine Learning and Cybernetics, International Journal of Applied Pattern Recognition, International Journal of Advances in Robotics Research and also on the editorial advisory board of the International Journal of Neural Systems.

Author Articles
Robust Face Detection integrating Novel Skin Color Matching under Variant Illumination Conditions

By Asif Anjum Akash M. A. H. Akhand N. Siddique

DOI: https://doi.org/10.5815/ijigsp.2021.02.01, Pub. Date: 8 Apr. 2021

Integration of skin color property in face detection algorithm is a recent trend to improve accuracy. The existing skin color matching techniques are illumination condition dependent, which directly impacts the face detection algorithm. In this study, a novel illumination condition invariant skin color matching method is proposed which is a composite of two rules to balance the high and low intensity facial images by individual rule. The proposed skin color matching method is incorporated into Haar Feature based Face Detection (HFFD) algorithm for face detection and is verified on a large set of images having variety of skin colors and also varying illumination intensities. Experimental results reveal the effectiveness and robustness of the proposed method outperforming other existing methods.

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