A survey on face presentation attack detection mechanisms: hitherto and future perspectives

Abstract

The advances in human face recognition (FR) systems have recorded sublime success for automatic and secured authentication in diverse domains. Although the traditional methods have been overshadowed by face recognition counterpart during this progress, computer vision gains rapid traction, and the modern accomplishments address problems with real-world complexity. However, security threats in FR-based systems are a growing concern that offers a new-fangled track to the research community. In particular, recent past has witnessed ample instances of spoofing attacks where imposter breaches security of the system with an artifact of human face to circumvent the sensor module. Therefore, presentation attack detection (PAD) capabilities are instilled in the system for discriminating genuine and fake traits and anticipation of their impact on the overall behavior of the FR-based systems. To scrutinize exhaustively the current state-of-the-art efforts, provide insights, and identify potential research directions on face PAD mechanisms, this systematic study presents a review of face anti-spoofing techniques that use computational approaches. The study includes advancements in face PAD mechanisms ranging from traditional hardware-based solutions to up-to-date handcrafted features or deep learning-based approaches. We also present an analytical overview of face artifacts, performance protocols, and benchmark face anti-spoofing datasets. In addition, we perform analysis of the twelve recent state-of-the-art (SOTA) face PAD techniques on a common platform using identical dataset (i.e., REPLAY-ATTACK) and performance protocols (i.e., HTER and ACA). Our overall analysis investigates that despite prevalent face PAD mechanisms demonstrate potential performance, there exist some crucial issues that requisite a futuristic attention. Our analysis put forward a number of open issues such as; limited generalization to unknown attacks, inadequacy of face datasets for DL-models, training models with new fakes, efficient DL-enabled face PAD with smaller datasets, and limited discrimination of handcrafted features. Furthermore, the COVID-19 pandemic is an additional challenge to the existing face-based recognition systems, and hence to the PAD methods. Our motive is to present a complete reference of studies in this field and orient researchers to promising directions.

Biometric Authentication based on Liveness Detection Using Face Landmarks and Deep Learning Model

“…The related attacks are especially dangerous in high-security environments where access to sensitive information or restricted areas is strictly controlled. Make face recognition technology in the banking industry an example, and false image attacks could allow unauthorized individuals to access bank accounts and conduct malicious transactions [2], [5]. The articles below also discuss the current weaknesses of the face recognition system and potential issues when confronted with a strong attack using fake images.…”

“…Some countries, example China have implemented this technology anywhere. Face recognition market growth is driven by rising demand for improved security and surveillance systems in various industries, including government, healthcare, and retail [2]. However, ethical concerns related to face recognition and liveness technologies, such as privacy, biases, and discrimination, are crucial.…”

Parallel Hybrid Algorithm for Face Recognition Using Multi-Linear Methods

“…Facial recognition technology has become an integral part of modern life, permeating various devices and systems, including cell phones, laptops, and surveillance equipment [1]. Its applications span security, access control, biometrics, and human-computer interaction [2] revolutionising different domains and enhancing user experiences [3] [4].…”