Few-shot palmprint recognition based on similarity metric hashing network

Palmprint recognition is one of the effective biometric technologies due to the advantages of convenience and safety. Recently, many deep learning-based methods are utilized in palmprint recognition and achieve satisfactory results. However, most of the existing learning methods are driven by the abundant labeled data. When the training data are insufficient, their performance drop sharply. In this work, we propose a novel and effective end-to-end algorithm for few-shot palmprint recognition, called Similarity Metric Hashing Network (SMHNet). SMHNet is designed to extract the features of palmprint images on both the structural and pixel levels. Specifically, an embedded structural similarity (SSIM) index block is constructed behind the last convolution layer to measure the structural similarity between query samples and support ones. A novel SSIM loss is designed with distance loss to train the entire model from scratch. Furthermore, a hashing block is added after the last fully connected (FC) layer to encode the features into hashing codes, which is convenient for large-scale feature storage and retrieval. Extensive experiments are conducted on three benchmark palmprint databases, and the results demonstrate that our model can achieve competitive accuracy compared with several state-of-the-art models.