Work place: Majmaah University, Al Majmaah 11952, Saudi Arabia
Research Interests: Software Engineering, Computer Networks, Image Processing, Information Engineering
Dr. Khalid Nazim Sattar Abdul is a Assistant Professor in the Computer Science and Information Department, College of Science, Al-Zulfi Campus, Majmaah University. He received his BE Degree in Computer Science & Engineering from Ghousia College of Engineering, Bangalore University, Bangalore. He obtained his master’s degree in computer science & Engineering from VTU, Belagavi. He was awarded PhD in Computer Science & Engineering from School of Computer Science, Singhania University, Rajasthan. He has over 33 peer-reviewed research articles in international journals and conference proceedings. His research interests include Image processing, Software Engineering, Information Systems, Computer Networks.
DOI: https://doi.org/10.5815/ijigsp.2023.05.05, Pub. Date: 8 Oct. 2023
We are aiming in this work to develop an improved face recognition system for person-dependent and person-independent variants. To extract relevant facial features, we are using the convolutional neural network. These features allow comparing faces of different subjects in an optimized manner. The system training module firstly recognizes different subjects of dataset, in another approach, the module processes a different set of new images. Use of CNN alone for face recognition has achieved promising recognition rate, however many other works have showed declined in recognition rate for many complex datasets. Further, use of CNN alone exhibits reduced recognition rate for large scale databases. To overcome the above problem, we are proposing a modified spatial texture pattern extraction technique namely modified Histogram oriented gradient (m-HOG) for extracting facial image features along three gradient directions along with CNN algorithm to classify the face image based on the features. In the preprocessing stage, the face region is captured by removing the background from the input face images and is resized to 100×100. The m-HOG features are retrieved using histogram channels evenly distributed between 0 and 180 degrees. The obtained features are resized as a matrix having dimension 66×198 and which are passed to the CNN to extract robust and discriminative features and are classified using softmax classification layer. The recognition rates obtained for L-Spacek, NIR, JAFFE and YALE database are 99.80%, 91.43%, 95.00% and 93.33% respectively and are found to be better when compared to the existing methods.[...] Read more.
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