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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 rotationinvariant, and therefore a great deal of preprocessing is needed to align theimages. In this paper, a powerful image representation, called scatteringnetwork/transform, is used for palmprint recognition. Scattering network is aconvolutional network where its architecture and filters are predefined wavelettransforms. The first layer of scattering network captures similar features toSIFT descriptors and the higher-layer features capture higher-frequency contentof the signal which are lost in SIFT and other similar descriptors. Afterextraction of the scattering features, their dimensionality is reduced byapplying principal component analysis (PCA) which reduces the computationalcomplexity of the recognition task. Two different classifiers are used forrecognition: multi-class SVM and minimum-distance classifier. The proposedscheme has been tested on a well-known palmprint database and achieved accuracyrate of 99.95% and 100% using minimum distance classifier and SVM respectively.

Palmprint Recognition Using Deep Scattering Convolutional Network