The use of Biometric Facial Recognition (FR) systems have become increasingly widespread, especially since the advent of deep neural network-based architectures. Although FR systems provide substantial benefits in terms of security and safety, the use of these systems also raises significant privacy concerns. This article discusses recent advances in facial identity hiding techniques, focusing on privacy protection approaches that hide or protect facial biometric data before camera devices capture the data. Moreover, we also discuss the state-of-the-art methods used to evaluate such privacy protection techniques. The primary motivation of this survey is to assess the relative performance of facial privacy protection methods and identify open challenges and future work that needs to be considered in this research area.

User authentication for an accurate biometric system is the demand of the hour in today’s world. When somebody attempts to take on the appearance of another person by introducing a phony face or video before the face detection camera and gets illegitimate access, a face presentation attack usually happens. To effectively protect the privacy of a person, it is very critical to build a face authentication and anti-spoofing system. This paper introduces a novel and appealing face spoof detection technique, which is primarily based on the study of contrast and dynamic texture features of both seized and spoofed photos. Valid identification of photo spoofing is anticipated here. A modified version of the DoG filtering method, and local binary pattern variance (LBPV) based technique, which is invariant to rotation, are designated to be used in this paper. Support vector machine (SVM) is used when feature vectors are extracted for further analysis. The publicly available NUAA photo-imposter database is adapted to test the system, which includes facial images with different illumination and area. The accuracy of the method can be assessed using the false acceptance rate (FAR) and false rejection rate (FRR). The results express that our method performs better on key indices compared to other state-of-the-art techniques following the provided evaluation protocols tested on a similar dataset.