Latent Fingerprint Value Prediction: Crowd-based Learning

Published: January 01, 2018

Author(s)

Elham Tabassi, Anil K. Jain, Tarang Chugh, Kai Cao, Jiayu Zhou

Abstract

Latent fingerprints are one of the most crucial sources of evidence in forensic investigations. As such, devel- opment of automatic latent fingerprint recognition systems to quickly and accurately identify the suspects is one of the most pressing problems facing fingerprint researchers. One of the first steps in manual latent processing is for a fingerprint examiner to perform a triage by assigning one of the following three values to a query latent: Value for Individualization (VID), Value for Exclusion Only (VEO) or No Value (NV). However, latent value determination by examiners is known to be subjective, resulting in large intra-examiner and inter-examiner variations. Furthermore, in spite of the guidelines available, the underlying bases that examiners implicitly use for value determination are unknown. In this paper, we propose a crowdsourcing based framework for understanding the underlying bases of value assignment by fingerprint examiners, and use it to learn a predictor for quantitative latent value assignment. Experimental results are reported using four latent fingerprint databases, two from forensic casework (NIST SD27 and MSP) and two collected in laboratory settings (WVU and IIITD), and a state-of-the-art latent AFIS. The main conclusions of our study are as follows: (i) crowdsourced latent value is more robust than prevailing value determination (VID, VEO and NV) and LFIQ for predicting AFIS performance, (ii) two bases can explain expert value assignments which can be interpreted in terms of latent features, and (iii) our value predictor can rank a collection of latents from most informative to least informative.
Citation: IEEE Transactions on Information Forensics and Security
Volume: 13
Pub Type: Journals

Keywords

Latent value determination, latent matching, latent examiners, crowdsourcing, matrix completion, multidimen- sional scaling, machine learning
Created January 01, 2018, Updated November 10, 2018