LigPPFNet: A Lightweight Multimodal Fusion Method for Palmprint and Palm Vein Recognition

Abstract

Due to their high stability, accuracy, and security, palmprint and palm vein recognition offer significant advantages in biometric applications. However, traditional deep neural network models typically suffer from high computational complexity and large parameters, which limit their application in practical embedded devices and mobile terminals. To address this limitation, we propose a novel lightweight palmprint and palm vein fusion network (LigPPFNet) for multimodal recognition. To address this limitation, we propose LigPPFNet, a lightweight multimodal fusion network for palmprint and palm vein recognition. We first construct PPFNet as the base multimodal fusion architecture, which employs a cross-modal high-order fusion mechanism to promote feature interaction and complementarity between palmprint and palm vein features. Building upon PPFNet, we further develop LigPPFNet as the final lightweight model through a series of targeted structural simplifications. Specifically, we simplify the feature fusion module, reduce the channel scale of the competitive blocks, remove the computationally expensive attention structure, and redesign the classification head. Extensive experiments on three public datasets show that, under the adopted experimental protocol, LigPPFNet achieved 100% recognition accuracy. Compared with its base architecture, LigPPFNet reduced the number of parameters by 97.8% (from 818.08 M to 17.86 M) and FLOPs by 93.9% (from 952.97 M to 58.48 M), indicating its potential for resource-constrained biometric applications.