Autor(es)

Nobrega, Andre ; Theodoro, Ilan ; Figueroa, Pascual ; Falcao, Alexandre Xavier

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION LETTERS; v. 198, p. 7-pg., 2025-12-01.

Resumo

Latent fingerprints are challenging to identify due to low quality, partial impressions, and noise. This paper proposes a self-supervised contrastive learning approach to generate minutiae embeddings, improving fingerprint representation and matching. We first introduce a method to synthesize realistic latent fingerprints from rolled and plain images by applying ridge distortions, contrast shifts, blurring, noise, and document-based backgrounds. The resulting dataset includes reliable minutiae correspondences for effective training. Fingerprints are then represented as orientation-aligned, minutia-centered patches. A Siamese network trained with contrastive learning on these patches produces discriminative embeddings. Matching computes the mean cosine similarity between the embeddings of paired minutiae from candidate references selected by a matcher. Experiments on NIST SD27 and SD302, using a 20,473-print gallery, demonstrate rank-1 identification gains of 4.25 and 1.66 percentage points over prior work. It also consistently outperforms other synthetic latent generation baselines. (AU)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 23/14427-8 - Ciência de Dados para a Indústria Inteligente (CDII)
• Beneficiário: José Alberto Cuminato
• Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa Aplicada