Akademik Veri Yönetim Sistemi
Neural Computing and Applications, cilt.36, sa.4, ss.1997-2014, 2024 (SCI-Expanded)
Biometric authentication systems identify or verify a person from a digital image taken by security cameras or fingerprint readers. Digital images are used for authentication wherever a security system exists, such as in airports and banks. Although biometric data authentication boosts security, it has several practical challenges and is a difficult problem in computer vision. Another application classifies biometric data according to certain characteristics such as age, gender, or race. One of the biometric data frequently used for this purpose and has become very important is face images. Deep learning systems can learn rich, compact representations of faces from very big face datasets, allowing people to surpass their facial analysis talents. The Convolutional Neural Network (CNN) has recently obtained very promising face analysis results among these methods. Although CNN has the beneficial use of the data’s correlation information, it has trouble learning efficiently when the supplied amount of the data or model is too huge. Quantum Convolutional Neural Network (QCNN) provides a new solution to a CNN-related problem using a quantum computing environment. In this study, gender recognition is performed with CNN and QCNN algorithms, and the results are compared in terms of time and accuracy. The purpose of the study is to show the comparative evaluation of QCNN and its classical counterpart CNN algorithms with detailed applications under the same conditions. 92% accuracy for QCNN and 90% accuracy for CNN are obtained. The total processing time is 128.85 s for QCNN and 832.30 s for CNN.