Palmprint recognition based on CNN and local coding features

Abstract

The convolutional neural network(CNN) has received an excellent performance in the recent tasks of image retrieval and image classification. Besides, some local coding methods have been focused on because of their outstanding local description. In this paper, we present a novel method for palmprint recognition, which combines CNN and local coding techniques. We conduct feature extraction on palmprint images using the pre-trained networks and obtain the local convolutional features. Then three different local coding methods are adopted to code the features from CNN, including Bag of Visual Word(BoVW), Locality-constrained Linear Coding(LLC) and Vector of Locally Aggregated Descriptors (VLAD). The features extracted from CNN have better representation. Meanwhile, the local coding makes local features more important. Hence the combination of them can get the anticipated better results. The proposed method is extensively evaluated on the PolyU palmprint database and PolyU multispectral palmprint database, illuminating the remarkable performance on palmprint recognition.