Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.

A novel way of managing the compromise between noise reduction and speech distortion in Wiener filters is presented. It is based on adjusting the amount of noise reduced, and therefore the speech distortion introduced, on a phone-by-phone basis. We show empirically that optimal Wiener filters produce different amounts of speech distortion for different phones. Therefore we propose a phone-conditioned suboptimal Wiener filter that uses different amounts of noise reduction for each phone, based on a previous estimation of the amount of distortion introduced. Speech recognition results have shown that phone conditioning suboptimal Wiener filtering can provide almost a 5% additional relative improvement in word accuracy over comparable optimal Wiener filtering.

This work studies the performance of a state-of-the-art fingerprint recognition technology, in several practical scenarios of interest in forensic casework. First, the differences in performance between manual and automatic minutiae extraction for latent fingerprints are presented. Then, automatic minutiae extraction is analyzed using three different types of fingerprints: latent, rolled and plain. The experiments are carried out using a database of latent finger marks and fingerprint impressions from real forensic cases. The results show high performance degradation in automatic minutiae extraction compared to manual extraction by human experts. Moreover, high degradation in performance on latent finger marks can be observed in comparison to fingerprint impressions.