Peter A. Johnson

Multimodal fusion vulnerability to non-zero effort (spoof) imposters

In biometric systems, the threat of “spoofing”, where an imposter will fake a biometric trait, has lead to the increased use of multimodal biometric systems. It is assumed that an imposter must spoof all modalities in the system to be accepted. This paper looks at the cases where some but not all modalities are spoofed. The contribution of this paper is to outline a method for assessment of multimodal systems and underlying fusion algorithms. The framework for this method is described and experiments are conducted on a multimodal database of face, iris, and fingerprint match scores.

Quality in face and iris research ensemble (Q-FIRE)

Identification of individuals using biometric information has found great success in many security and law enforcement applications. Up until the present time, most research in the field has been focused on ideal conditions and most available databases are constructed in these ideal conditions. There has been a growing interest in the perfection of these technologies at a distance and in less than ideal conditions, i.e. low lighting, out-of-focus blur, off angles, etc. This paper presents a dataset consisting of face and iris videos obtained at distances of 5 to 25 feet and in conditions of varying quality. The purpose of this database is to set a standard for quality measurement in face and iris data and to provide a means for analyzing biométrie systems in less than ideal conditions. The structure of the dataset as well as a quantified metric for quality measurement based on a 25 subject subset of the dataset is presented.

Increase the security of multibiometric systems by incorporating a spoofing detection algorithm in the fusion mechanism

The use of multimodal biometric systems has been encouraged by the threat of spoofing, where an impostor fakes a biometric trait. The reason lies on the assumption that, an impostor must fake all the fused modalities to be accepted. Recent studies showed that there is a vulnerability of the existing fusion schemes in presence of attacks where only a subset of the fused modalities is spoofed. In this paper, we demonstrated that, by incorporating a liveness detection algorithm in the fusion scheme, the multimodal system results robust in presence of spoof attacks involving only a subset of the fused modalities. The experiments were carried out by analyzing different fusion rules on the Biosecure multimodal database.

Impact of out-of-focus blur on face recognition performance based on modular transfer function

It is well recognized that face recognition performance is impacted by the image quality. As face recognition is increasingly used in semi-cooperative or unconstrained applications, quantifying the impact of degraded image quality can provide the basis for improving recognition performance. This study uses a range of real out-of-focus blur obtained by controlled changes of the focal plane across face video sequences during acquisition from the Q-FIRE dataset. The modulation transfer function (MTF) method for measuring sharpness is presented and compared with other sharpness measurements with a reference of the co-located optical chart. Face recognition performance is then examined at eleven sharpness levels based on the MTF quality metrics. Experimental results show the MTF quality metrics better quantify a range of blur compared to the optical chart and offer a useful range of interest for face recognition performance. This paper demonstrates the applicability of an image blur quality metric as auxiliary information to supplement face recognition systems through the analysis of a unique database.

Texture Modeling for Synthetic Fingerprint Generation

The development of biometric recognition technologies often requires large sets of biometric data for training and evaluation purposes. The use of synthetically generated biometric samples has been explored as a means of avoiding the challenges of large scale data collection. Our paper builds on previous work in synthetic fingerprint generation research through the modeling and synthesis of texture characteristics for synthetic fingerprint generation. The proposed texture characterizing features can be modeled from real fingerprint images to generate synthetic fingerprint texture statistically representative of a particular real fingerprint database. The texture characterizing features include ridge intensity along the ridge center-lines with seven frequency components, ridge width, ridge cross-sectional slope, ridge noise, and valley noise. A comparison of these feature densities from real and synthetic fingerprints is shown, which demonstrates the effectiveness of this method of modeling and generating synthetic fingerprint textures.

Comparison of quality-based fusion of face and iris biometrics

Multimodal systems have been used for the increased robustness of biometric recognition tasks. A unique strength of multimodal systems can be found when presented with biometric samples of degraded quality in a subset of the modalities. This study looks at the effect of quality degradation on system performance using the Q-FIRE database. The Q-FIRE database is a multimodal database composed of face and iris biometrics captured at defined quality levels, controlled at acquisition. This database allows for assessment of biometric system performance pertaining to image quality factors. Methods for measuring image quality based on illumination conditions are explored as well as strategies for incorporating these quality metrics into a multimodal fusion algorithm. This paper provides further evidence in a unique dataset that utilizing sample quality metrics into the fusion scheme of a multimodal system improves system performance in non-ideal acquisition environments.

Impact of out-of-focus blur on iris recognition

Iris recognition has expanded from controlled settings to uncontrolled settings (on the move, from a distance) where blur is more likely to be present in the images. More research is needed to quantify the impact of blur on iris recognition. In this paper we study the effect of out-of-focus blur on iris recognition performance from images captured with out-of-focus blur produced at acquisition. A key aspect to this study is that we are able to create a range of blur based on changing focus of the camera during acquisition. We quantify the produced out-of-focus blur based on the Laplacian of Gaussian operator and compare it to the gold standard of the modulation transfer function (MTF) of a calibrated black/white chart. The sharpness measure uses an unsegmented iris images from a video sequence with changing focus and offers a good approximation of the standard MTF. We examined the effect of the 9 blur levels on iris recognition performance. Our results have shown that for moderately blurry images (sharpness at least 50%) the drop in performance does not exceed 5% from the baseline (100% sharpness).