Face Spoofing Detection Using 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. This paper introduces a novel and appealing approach for detecting face spoofing using a colour texture analysis. 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. More specifically, the feature histograms are computed over each image band separately. 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 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. 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.

I. Introduction

Nowadays, it is known that most of existing face recognition systems are vulnerable to spoofing attacks. A spoofing attack occurs when someone tries to bypass a face biometric system by presenting a fake face in front of the camera. For instance, in [1], researchers inspected the threat of the online social networks based facial disclosure against the latest version of six commercial face authentication systems (Face Unlock, Facelock Pro, Visidon, Veriface, Luxand Blink and FastAccess). While on average only 39% of the images published on social networks can be successfully used for spoofing, the relatively small number of usable images was enough to fool face authentication software of 77% of the 74 users. Also, in a live demonstration during the International Conference on Biometric (ICB 2013), a female intruder with a specific make-up succeeded in fooling a face recognition system. These two examples among many others highlight the vulnerability of face recognition systems to spoofing attacks.¹

https://www.tabularasa-euproject.org/evaluations/tabula-rasa-spoofing-challenge-2013