Detekce živosti otisku prstu na bezdotykovém zařízení

This Bachelor's Thesis is focused on liveness detection of fingerprints with using touchless sensor. Work summarizes theoretical introduction to biometrics, fingerprint processing and some of present researches for liveness detection. The new approach is introduced with using Local Binary Pattern algorithm, Sobel and Laplacian operator and Wavelet transform. Artificial Neural Networks, Support Vector Machines and Decision Trees were used for final classification. Several experiments with dataset illuminated by lights with various wavelengths were realized. It was discovered, that fingerprints illuminated by red light reached the best accuracy 90.1% compared to other considered wavelenghts of visible light. The classification with vector based on Local Binary Pattern achieved average accuracy 89.8%, accuracy with vector based on Sobel and Laplacian operator was 91.5%. Several Wavelet families were used for Wavelet transform during experiments. The best accuracy achieved wavelets of Biorthogonal spline wavelet family (85.1%) and wavelets from Reverse biorthogonal spline wavelet family (86.6%).

biometrics, liveness detection, local binary pattern, image processing, touchless scanner, wavelet transform, fingerprints