Gottschlich, C. (2016). Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection PLoS ONE, 11(2), e0148552.
Gottschlich, C., Huckemann, S. (2014). Separating the Real From the Synthetic: Extended Minutiae Histograms as Fingerprints of Fingerprints. IET Biometrics, 3(4), 291-301.
Gottschlich, C. (2012). Curved-Regions-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement IEEE Transactions on Image Processing, 21(4), 2220-2227.
Gottschlich, C., Mihailescu, P., Munk, A. (2009). Robust Orientation Field Estimation and Extrapolation Using Semilocal Line Sensors IEEE Transactions on Information Forensics and Security, 4(4), 802-811.
In our biometrics group, we work on different challenges related to the recognition of humans with help of their biometric characteristics. In particular, we investigate methods for fingerprint idenfication, liveness detection, image enhancement, fingerprint growth modelling as well as on problems concerned with the critical question how to store fingerprint templates securely in databases.
Software-based liveness detection is a suitable countermeasure against presentation attacks by fake fingers made from material like silicon or gelatine. The figure above gives an overview over the computation of convolution comparison patterns (CCP) for liveness detection.