Christof Kauba

Sensor ageing impact on finger-vein recognition

The impact of sensor ageing related pixel defects on the performance of finger vein based recognition systems in terms of the EER (Equal Error Rate) is investigated. Therefore the defect growth rate per year for the sensor used to capture the data set was estimated. Based on this estimation an experimental study using several simulations with increasing numbers of stuck and hot pixels were done to determine the impact on different finger-vein matching schemes. Whereas for a reasonable number of pixel defects none of the methods is considerably influenced, the performance of several schemes drops if the number of defects is increased. The impact can be reduced using a simple denoising filter.

Robustness Evaluation of Hand Vein Recognition Systems

Hand vein recognition systems are more robust against external influences which degrade the image quality like dust or dirt on the sensor or skin surface conditions than fingerprint ones. We investigate the robustness of several hand vein feature extraction and matching schemes against different types of image distortions, related to conditions occurring during the acquisition of hand vein images. These distortions correspond to sensor defects, bad system design and problems in the use of the sensor. The impact on the recognition accuracy is quantified in terms of the EER and compared across the different schemes and different types of distortions.

Towards Drug Counterfeit Detection Using Package Paperboard Classification

Most approaches for product counterfeit detection are based on identification using some unique marks or properties implemented into each single product or its package. In this paper we investigate a classification approach involving existing packaging only in order to avoid higher production costs involved with marking each individual product. To detect counterfeit packages, images of the package’s interior showing the plain structure of the paperboard are captured. Using various texture features and SVM classification we are able to distinguish drug packages coming from different manufacturers and also forged packages with high accuracy while a distinction between single packages of the same manufacturer is not possible.

PRNU-based image alignment for defective pixel detection

Image alignment or registration is needed for many tasks. Conventional techniques are based on the image content. For forensic purposes where the characteristics of the image sensor itself are investigated, alignment has to be done based on the pixel grid the sensor used to capture the images. Biometric sensor analysis is one example for which this kind of alignment is needed. Our method utilises the photo response non uniformity (PRNU) fingerprint of the biometric sensor to align the images. At first the PRNU noise residual is extracted and enhanced. Then the shifts of the images are corrected by determining peaks in the normalised cross correlation results of the individual images. One possible use case where such aligned images are needed is the detection of sensor ageing related pixel defects in the scope of the biometric template ageing phenomenon but our approach can be useful for many other tasks in the field of biometric sensor analysis.

Fingerprint recognition under the influence of image sensor ageing

Fingerprint recognition performance is affected by many factors. One of these is defective pixels caused by ageing effects of the image sensor. The authors investigate the impact of these image sensor ageing related pixel defects on the performance of different fingerprint (NBIS, VeriFinger, FingerCode and Phase Only Correlation) recognition systems. Their performances are compared against each other to quantify the differences in the impact. In practice, besides image sensor ageing related effects, other influences are also present. As the authors aim to evaluate the impact of the defective pixels only, disregarding subject ageing and other external influences, it is not possible to use real image data. Instead, an experimental study utilising an ageing simulation algorithm introducing hot and stuck pixels is conducted on the FVC2002 and FVC2004 data sets, including tests with different denoising approaches trying to mitigate the effects of image sensor ageing while maintaining the baseline recognition accuracy.