Face recognition is a well-researched domain however many issues for instance expression changes, illumination variations, and presence of occlusion in the face images seriously affect the performance of such systems. A recent survey shows that COVID-19 will also have a considerable and long-term impact on biometric face recognition systems. The work has presented two novel Savitzky–Golay differentiator (SGD) and gradient-based Savitzky–Golay differentiator (GSGD) feature extraction techniques to elevate issues related to face recognition systems. The SGD and GSGD feature descriptors are able to extract discriminative information present in different parts of the face image. In this paper, an efficient and robust person identification using symbolic data modeling approach and similarity analysis measure is devised and employed for feature representation and classification tasks to address the aforementioned issues of face recognition. Extensive experiments and comparisons of the proposed descriptors experimental results indicated that the proposed approaches can achieve optimal performance of 96–97, 92–96, 100, 84–93, and 87–96% on LFW, ORL, AR, IJB-A datasets, and newly devised VISA database, respectively.

人脸识别是一个经过充分研究的领域，但是诸如表情变化、光照变化和人脸图像中存在遮挡等许多问题都会严重影响此类系统的性能。最近的一项调查显示，COVID-19 还将对生物特征人脸识别系统产生相当大的长期影响。这项工作提出了两种新颖的 Savitzky-Golay 微分器 (SGD) 和基于梯度的 Savitzky-Golay 微分器 (GSGD) 特征提取技术，以提升与人脸识别系统相关的问题。SGD 和 GSGD 特征描述符能够提取存在于人脸图像不同部分的判别信息。在本文中，设计了一种使用符号数据建模方法和相似性分析度量的有效且稳健的人员识别方法，并将其用于特征表示和分类任务，以解决上述人脸识别问题。广泛的实验和对所提出的描述符的比较实验结果表明，所提出的方法可以在 LFW、ORL、AR、IJB-A 数据集上实现 96-97、92-96、100、84-93 和 87-96% 的最佳性能, 和新设计的 VISA 数据库。