Comparative Experimental Investigation of Deep Convolutional Neural Networks for Latent Fingerprint Pattern Classification

Creative Commons License

Noğay H. S.

TRAITEMENT DU SIGNAL, cilt.38, sa.5, ss.1319-1326, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 38 Sayı: 5
  • Basım Tarihi: 2021
  • Doi Numarası: 10.18280/ts.380506
  • Dergi Adı: TRAITEMENT DU SIGNAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Business Source Elite, Business Source Premier, Compendex, zbMATH
  • Sayfa Sayıları: ss.1319-1326
  • Anahtar Kelimeler: fingerprint, deep learning, transfer, learning, DCNN, pattern recognition, RECOGNITION SYSTEMS, BIAS
  • Bursa Uludağ Üniversitesi Adresli: Hayır

Özet

Fingerprint pattern recognition is of great importance in forensic examinations and in helping diagnose some diseases. The automatic realization of fingerprint recognition processes can take time due to the feature extraction process in classical machine learning or deep learning methods. In this study, the effective use of deep convolutional neural networks (DCNN) in fingerprint pattern recognition and classification, in which feature extraction takes place automatically, was examined experimentally and comparatively. Five DCNN models have been designed and implemented with a transfer learning approach. Four of these five models are Alexnet, Googlenet, Resnet-18, and Squeezenet pre-trained DCNN models. The fifth model is the DCNN model designed from the ground up. It was concluded that the designed DCNN models can be used effectively in fingerprint recognition and classification, and that fast results can be obtained and generalized with advanced DCNN models.