Benchmarking Neural Network Compression Techniques for Ocular-Based User Authentication on Smartphones
Benchmarking Neural Network Compression Techniques for Ocular-Based User Authentication on Smartphones
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DOI：
10.1109/access.2023.3265357
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发表时间：
2023
期刊：
影响因子：
3.9
通讯作者：
Ali Almadan;A. Rattani
中科院分区：
文献类型：
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作者：
Ali Almadan;A. Rattani
随着前所未有的移动的技术革命，移动的设备已经从主要的通信手段超越为一体化平台。因此，越来越多的人通过智能手机而不是传统的台式电脑访问电子商务和银行的在线服务。然而，智能电话可能比其他计算设备更频繁地容易被错放、丢失或被盗，从而需要用于设备解锁和安全交易的有效用户认证机制。由于其准确性、安全性以及在移动的设备中的易用性，眼部生物识别技术已经获得了学术界和工业界的极大关注。一些研究已经证明了深度学习模型在智能手机上基于视觉的用户身份验证中的有效性。然而，这些高性能模型需要巨大的空间和计算复杂性，因为涉及数百万个参数和计算。这些要求使得它们在资源受限的智能手机上的部署具有挑战性。为此，已经提出了一些研究，用于紧凑尺寸的基于眼睛的深度学习模型，以促进设备上的部署。在本文中，我们对现有的神经网络压缩技术进行了深入的分析，这些技术作为独立的和组合的应用于基于视觉的用户身份验证。对两个最新的使用智能手机收集的大规模眼部生物特征数据集，即UFPR和VISOB 2.0数据集进行了广泛的实验验证。这项研究基准的结果，先进的压缩技术，进一步研究和开发的轻量级模型，基于视觉的用户身份验证的智能手机。
With the unprecedented mobile technology revolution, mobile devices have transcended from being the primary means of communication to an all-in-one platform. Consequently, an increasing number of individuals are accessing online services for e-commerce and banking via smartphones instead of traditional desktop computers. However, smartphones can be easily misplaced, lost, or stolen more often than other computing devices, thereby demanding effective user authentication mechanisms for device unlocking and secured transactions. Ocular biometrics has obtained significant attention from academia and industry because of its accuracy, security, and ease of use in mobile devices. Several studies have demonstrated the efficacy of deep learning models for ocular-based user authentication on smartphones. However, these high-performing models require enormous space and computational complexity due to the millions of parameters and computations involved. These requirements make their deployment on resource-constrained smartphones challenging. To this end, a handful of studies have been proposed for compact-size ocular-based deep-learning models to facilitate on-device deployment. In this paper, we conduct a thorough analysis of the existing neural network compression techniques applied as a standalone and in combination for ocular-based user authentication. Extensive experimental validation is performed on the two latest large-scale ocular biometric datasets collected using smartphones, namely, UFPR and VISOB 2.0 datasets. This study benchmarks the results of advanced compression techniques for further research and development in lightweight models for ocular-based user authentication on smartphones.