A review study on latent fingerprint recognition techniques

Fingerprints verification based on their spectrum

“…The two experiments were run 12 times for the 12 FVCs databases. The results in figures (11)(12)(13) show that, the FMR and FNMR of spectrum based verification approach are less than the FMR and FNMR of minutiae based verification approach. These results The results ensure the fidelity of the spectrum based verification approach to the minutiae based verification approach and the other published results over the different FVCs databases.…”

“…The online acquisition is done by pressing the finger against a flat surface of an electronic fingerprint sensor; the sensors may be optical, Features matching enhancement has been carried out using many approaches and filters such as adaptive histogram equalization [9][10][11]. A survey of the fingerprint image enhancement process is presented in [12]. In this paper; image enhancement is carried out using AHE and Gabor filter.…”

Level 2 feature extraction for latent fingerprint enhancement and matching using type-2 intuitionistic fuzzy set

“…Tang et al (2016) have proposed a method for latent fingerprint minutia extraction using fully convolutional network. Ezhilmaran and Adhiyaman (2014) have surveyed about different fingerprint enhancement techniques and Cao and Jain (2017) proposed an automated latent fingerprint recognition algorithm that utilises convolutional neural networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. Cao et al (2014) have approached a dictionary-based method for automatic latent segmentation and enhancement in the direction of the objective of accomplishing 'lights-out' latent ID frameworks.…”

Recent Trends in Fluorescent Organic Materials for Latent Fingerprint Imaging

“…The second-level features (minutia points) are distinctive and stable, widely used for distinguishing the uniqueness of fingerprint, while the third-level features (pores and ridge contours) are the dimension attributes of the ridges to provide more accurate and robust details for accurate fingerprint recognition. Nowadays, many methods exist as standard methods for the development of the LFPs on common substrates in routine forensic practice (Ezhilmaran and Adhiyaman, 2017 ; Lennard, 2020 ), but there are still some situations that it is difficult or impossible to recover LFPs for forensic investigators. The ongoing research is being directed at improved sensitivity, universality, convenience, and efficiency via the optimization of existing methods or new approaches, such as spectroscopy, mass spectrometry, immuno-labeling, and nanoparticles based approaches (Nakamura et al, 2015 ; Zhao et al, 2016 ; Figueroa et al, 2017 ; O'Neill et al, 2018 ; Kolhatkar et al, 2019 ; Bodelón and Pastoriza-Santos, 2020 ; Li et al, 2020 ).…”