Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

  • Mustafa H. Mohammed Alhabib Department of Communications and Computer Engineering, Cihan University-Erbil, Kurdistan Region, Iraq.
  • Mustafa Zuhaer Nayef Al-Dabagh Department of Computer Science, Knowledge University, Kurdistan Region, Iraq
  • Firas H. AL-Mukhtar Department of Information Technology, Catholic University in Erbil, Kurdistan Region, Iraq
  • Hussein Ibrahim Hussein Department of Computer and Communication Engineering, Universiti Malaysia Perlis, Malaysia
Keywords: K-nearest neighborhood, Partial face recognition, Principal component analysis, Support vector machine, Wavelet transform


Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.


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How to Cite
Alhabib M, Al-Dabagh M, AL-Mukhtar F, Hussein H. Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition. cuesj [Internet]. 20Aug.2019 [cited 5Mar.2024];3(2):80-4. Available from: https://journals.cihanuniversity.edu.iq/index.php/cuesj/article/view/118
Research Article