Face Recognition in the Modern Era: A Survey of Techniques, Challenges, and Innovations

This review paper talks about the development of face recognition, ranging from traditional methods to the latest methods of deep learning (Hasan et al., 2021; Sáez-Trigueros et al., 2018). The early methods were based on separate characteristics like SIFT and LBP (Balaban, 2015) that could not handle complex scenarios. The use of statistical subspace methods helped to improve face representation. Deep learning, however, has transformed the domain, where systems like DeepFace attain nearly human-like performance by taking advantage of vast and various datasets (Taigman et al., 2014). Nonetheless, the bias, fairness, and privacy issues that remain unsolved have led to the ongoing research to make the face recognition systems more robust and ethically acceptable. Keywords--- Face recognition; Illuminations; partial occlusion; pose invariance