Quality Measures in Biometric Systems

Abstract

Biometric technology has been increasingly deployed in the last decade, offering greater security and convenience than traditional methods of personal recognition. But although the performance of biometric systems is heavily affected by the quality of biometric signals, prior work on quality evaluation is limited. Quality assessment is a critical issue in the security arena, especially in challenging scenarios (e.g. surveillance cameras, forensics, portable devices or remote access through Internet). Different questions regarding the factors influencing biometric quality and how to overcome them, or the incorporation of quality measures in the context of biometric systems have to be analysed first. In this paper, a review of the state-of-the-art in these matters is provided, giving an overall framework of the main factors related to the challenges associated with biometric quality.

Multiple classifiers in biometrics. Part 2: Trends and challenges

“…As a result, it was shown in several works that the performance of an unimodal system can drop significantly under noisy conditions [36]. Multimodal systems have been demonstrated to overcome this challenge to some extent by combining the evidences provided by a number of different traits.…”

“…Quality measures of the input biometric signals can be used for adapting the different modules of a multimodal authentication system [36]. Here we concentrate in quality-based score fusion.…”

Texture code matrix-based multi-instance iris recognition

“…Vatsa et al [23] used two types of distinct iris features, namely Log-Gabor and Euler numbers, and thus improved the recognition accuracy. Fernandez et al [24] represented the framework for the challenges of biometric quality which affects biometric system's performances. Lim et al [25] used a 2-d Haar wavelet transformation to decompose iris image into four levels and compute 87 features as iris signature, and then used LVQ neural network classifier to classify the iris images.…”

Evaluation of AFIS-Ranked Latent Fingerprint Matched Templates

“…The latent fingerprints which are the unintentional traces left behind by the perpetrator or by the victim are of poor quality in nature [9] [7] [3]. So, a reliable manual feature extraction is mainly influenced by the perception and decision making ability of a forensic examiner, which eventually affects the final decision.…”