This paper provides a comparative analysis of countermeasure techniques focusing on their performance on different face datasets for each identified threat. State-of-the-art approaches available in the literature are discussed here to mitigate the impact of the identified threats. These threats are described, considering their impact on the facial recognition system. This survey paper proposes a novel taxonomy to represent potential face identity threats. This paper significantly explores all types of face recognition techniques, their accountable challenges, and threats to face-biometric-based identity recognition. However, the face identity threats are evenly growing at the same rate and posing severe concerns on the use of face-biometrics. Facial recognition systems deliver highly meticulous results in every of these application domains. Face-biometric has a highly dominant role in various applications such as border surveillance, forensic investigations, crime detection, access management systems, information security, and many more. Facial recognition systems are in huge demand, next to fingerprint-based systems. Henceforth, the importance of face recognition systems is growing higher for many applications. The human face is considered the prime entity in recognizing a person’s identity in our society. The promising results of our cross-database evaluation suggest that the facial colour texture representation is more stable in unknown conditions compared with its gray-scale counterparts. More importantly, unlike most of the methods proposed in the literature, our proposed approach is able to achieve stable performance across all the three benchmark data sets. Extensive experiments on the three most challenging benchmark data sets, namely, the CASIA face anti-spoofing database, the replay-attack database, and the MSU mobile face spoof database, showed excellent results compared with the state of the art. More specifically, the feature histograms are computed over each image band separately. We exploit the joint colour-texture information from the luminance and the chrominance channels by extracting complementary low-level feature descriptions from different colour spaces. This paper introduces a novel and appealing approach for detecting face spoofing using a colour texture analysis. Research on non-intrusive software-based face spoofing detection schemes has been mainly focused on the analysis of the luminance information of the face images, hence discarding the chroma component, which can be very useful for discriminating fake faces from genuine ones.
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