Fingerprint classification through self-organizing feature maps modified to treat uncertainties

Halici U., ONGUN G.

PROCEEDINGS OF THE IEEE, cilt.84, sa.10, ss.1497-1512, 1996 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 84 Sayı: 10
  • Basım Tarihi: 1996
  • Doi Numarası: 10.1109/5.537114
  • Dergi Adı: PROCEEDINGS OF THE IEEE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1497-1512
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In this paper, a neural network structure based on Self Organizing Feature Maps (SOM) is proposed for fingerprint classification. In order to be able to deal with fingerprint images having distorted regions, the SOM learning and classification algorithms are modified. For this purpose, the concept of ''certainty'' is introduced and used in the modified algorithms. This fingerprint classifier together with a fingerprint identifier, constitute subsystems of an automated fingerprint identification system named HALafis.(1) Our results show that a network that is trained with a sufficiently large and representative set of samples can be used as an indexing mechanism for a fingerprint database, so that it does not need to be retained for each fingerprint added to the database.