Palmprint recognition using deep scattering network
Palmprint recognition has drawn a lot of attentions during recent years. Different features and algorithms have been proposed for palmprint recognition in the past such as Gabor-based features, wavelet features, and histogram of oriented lines. In this paper, a powerful image representation, so called deep scattering network, is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. Scattering transform is designed such that the features in its first layer are similar to SIFT descriptors and the higher layers' features capture higher frequency content of the signal which are lost in SIFT. After extraction of scattering features, their dimensionality is reduced by applying principal component analysis (PCA). By doing so, a great amount of computation complexity can be reduced. At the end, the recognition is performed using two different classifiers, multi-class SVM and minimum-distance classifier. The proposed scheme has been tested on a well-known palmprint database and achieved accuracy rates of 99.4% and 99.9% using minimum distance classifier and SVM respectively, outperforming previous algorithms on this dataset.