Hybrid User-Independent and User-Dependent Offline Signature Verification with a Two-Channel CNN

YILMAZ M. B., Ozturk K.

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Utah, Amerika Birleşik Devletleri, 18 - 22 Haziran 2018, ss.639-647 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/cvprw.2018.00094
  • Basıldığı Şehir: Utah
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.639-647
  • Akdeniz Üniversitesi Adresli: Evet

Özet

Signature verification task needs relevant signature representations to achieve low error rates. Many signature representations have been proposed so far. In this work we propose a hybrid user-independent/dependent offline signature verification technique with a two-channel convolutional neural network (CNN) both for verification and feature extraction. Signature pairs are input to the CNN as two channels of one image, where the first channel always represents a reference signature and the second channel represents a query signature. We decrease the image size through the network by keeping the convolution stride parameter large enough. Global average pooling is applied to decrease the dimensionality to 200 at the end of locally connected layers.