Deep Convolutional Features for Fingerprint Indexing

  • Leonardo F. da Costa UFC
  • Lucas S. Fernandes UFC
  • João P. B. Andrade UFC
  • Paulo A. L. Rego UFC
  • José G. R. Maia UFC

Resumo


Automatic fingerprint identification systems (AFIS) are among the most used people identification solutions. As the size of fingerprint databases is continuously growing, studying fingerprint indexing mechanisms is desirable to facilitate the search process in a large-scale database. This work presents a method for fingerprint indexing, which uses both exact and approximation methods of nearest neighbors (ANNs), which are very efficient in terms of runtime, even if they sacrifice a little accuracy by presenting approximate solutions. In the presented approach, searches with ANN methods are made from deep embedding vectors extracted from image databases using a convolutional neural network (CNN). In this work, a CNN ResNet18 was used to extract the deep feature embeddings vectors, and the vectors vary in size between 64, 96, and 128. The ANNs methods tested for the query step were ANNOY, NGT, HNSW, and Nanoflann. The results were quite promising when using the FVC fingerprint databases (2000, 2002, and 2004), once we reached 100% hits in the searches with a penetration rate of 1%, with very low run times.
Palavras-chave: Fingerprint indexing, Deep feature embedding, CNN, ANN models
Publicado
29/11/2021
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COSTA, Leonardo F. da; FERNANDES, Lucas S.; ANDRADE, João P. B.; REGO, Paulo A. L.; MAIA, José G. R.. Deep Convolutional Features for Fingerprint Indexing. In: BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS), 10. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 .