  Palmprint recognition has drawn a lot of attention during the recent years.
Many algorithms have been proposed for palmprint recognition in the past,
majority of them being based on features extracted from the transform domain.
Many of these transform domain features are not translation or rotation
invariant, and therefore a great deal of preprocessing is needed to align the
images. In this paper, a powerful image representation, called scattering
network/transform, is used for palmprint recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering network captures similar features to
SIFT descriptors and the higher-layer features capture higher-frequency content
of the signal which are lost in SIFT and other similar descriptors. After
extraction of the scattering features, their dimensionality is reduced by
applying principal component analysis (PCA) which reduces the computational
complexity of the recognition task. Two different classifiers are used for
recognition: multi-class SVM and minimum-distance classifier. The proposed
scheme has been tested on a well-known palmprint database and achieved accuracy
rate of 99.95% and 100% using minimum distance classifier and SVM respectively.
