Analisis Perbandingan Efektivitas Pra-Pengolahan Terhadap Reka Bentuk Sidik Jari Menggunakan Estimasi Orientasi

Estimasi orientasi merupakan suatu langkah penting dalam berbagai proses reka bentuk citra sidik jari, termasuk perbaikan dan klasifikasi sidik jari. Pada berbagai metode pengolahan citra sidik jari sebelum dilakukan langkah estimasi orientasi seringkali didahului oleh berbagai langkah pra-pengolahan. Berbagai langkah pra-pengolahan tersebut sedikit banyak memiliki pengaruh terhadap kualitas hasil estimasi orientasi. Untuk menguji pengaruh dari berbagai kombinasi pra-pengolahan tersebut akan digunakan perangkat lunak yang dibangun untuk penelitian ini. Perangkat lunak yang dihasilkan pada penelitian ini adalah suatu perangkat lunak yang mampu melakukan kombinasi pra-pengolahan dari tiga jenis pra-pengolahan yang umum dipakai, yaitu normalisasi, segmentasi, dan penghapusan derau, dan kemudian mampu melakukan estimasi orientasi, sehingga dapat dianalisis efektivitas masing-masing kombinasi pra-pengolahan terhadap estimasi orientasi. Dengan demikian dapat diketahui kombinasi pra-pengolahan manakah yang paling efekif untuk melakukan reka bentuk sidik jari menggunakan estimasi orientasi

A Review of Fingerprint Feature Representations and Their Applications for Latent Fingerprint Identification: Trends and Evaluation

Latent fingerprint identification is attracting increasing interest because of its important role in law enforcement. Although the use of various fingerprint features might be required for successful latent fingerprint identification, methods based on minutiae are often readily applicable and commonly outperform other methods. However, as many fingerprint feature representations exist, we sought to determine if the selection of feature representation has an impact on the performance of automated fingerprint identification systems. In this paper, we review the most prominent fingerprint feature representations reported in the literature, identify trends in fingerprint feature representation, and observe that representations designed for verification are commonly used in latent fingerprint identification. We aim to evaluate the performance of the most popular fingerprint feature representations over a common latent fingerprint database. Therefore, we introduce and apply a protocol that evaluates minutia descriptors for latent fingerprint identification in terms of the identification rate plotted in the cumulative match characteristic (CMC) curve. From our experiments, we found that all the evaluated minutia descriptors obtained identification rates lower than 10% for Rank-1 and 24% for Rank-100 comparing the minutiae in the database NIST SD27, illustrating the need of new minutia descriptors for latent fingerprint identification.This work was supported in part by the National Council of Science and Technology of Mexico (CONACYT) under Grant PN-720 and Grant 63894