Fingerprint recognition using translation invariant scattering network
Fingerprint recognition has drawn a lot of attention during the last few decades. Different features and algorithms have been used for fingerprint recognition in the past. In this paper, a powerful image representation called scattering transform/ network is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to SIFT descriptors and the higher layers capture higher frequency content of the signal. After extracting the scattering features, their dimensionality is reduced by applying principal component analysis (PCA). In the end, multi-class SVM is used to perform template matching for the recognition task. The proposed algorithm in this paper is one of the first works which explores the application of deep architecture for fingerprint recognition. The proposed scheme is tested on a well-known fingerprint database and has shown promising results with the best accuracy rate of 98%.