Paper—Face Patterns Analysis and Recognition System Based on Quantum Neural Network QNN Face Patterns Analysis and Recognition System Based on Quantum Neural Network QNN https://doi.org/10.3991/ijim.v16i08.30107 Haider TH. Salim ALRikabi 1(*) , Ibtisam A. Aljazaery 2 , Jaafar Sadiq Qateef 1 , Abdul Hadi M. Alaidi 1 , Roa’a M. Al_airaji 2 1 Wasit University, Wasit, Iraq 2 University of Babylon, Babylon, Iraq hdhiyab@uowasit.edu.iq Abstract—The past few years have witnessed a huge increase in the appli- cation of facial recognition, detection, and analysis technology. However, face recognition systems remain the most popular among the general public. The facial recognition system can detect the presence of a face when exposed to one. The accuracy and fairness that can be derived from such systems necessitate their use, because humans, particularly security personnel can be tired and target the wrong person as a suspect. However, artifcial intelligence systems that are prop- erly trained are capable of effciently identifying and classifying faces without errors. In this work, the use of Matlab language was employed in building a software system that is capable of recognizing and differentiating different face patterns. The proposed system is equipped with a camera that serves as the prac- tical aspect of the software that captures different shots that are sent to the theo- retical part of a special program that is designed to recognize faces by comparing them with a database stored within the program. The practical part of the work involved the use of Quantum Neural Network. In this work, the training dataset is made up of features vectors that were obtained from a well-known set of face images of different people. Here, Principle Component Analysis (PCA) was used for the extraction of feature vectors from images and then prepared for the next training step. The experimental results revealed that effcient face recognition can be achieved through the use of well-trained Quantum Network. Keywords—artifcial intelligence, facial recognition, feature extraction, QNN, PCA 1 Introduction Academics and industry professionals are increasingly becoming interested in the accuracy of computerized identifcation systems, due to the increasing security chal- lenges around the world [1]. This interest has resulted in a drastic increase in the number of security applications and systems. A wide variety of algorithms ranging from simple to complex ones have been developed to this end [2, 3]. However, the main question here is “how accurate is the facial recognition algorithm for security applications?” 34 http://www.i-jim.org