Intensity-Chromaticity-Luminance (ICL) Based Technique for Face Spoofing Detection

The significance of Face Recognition and Face Biometric Authentication systems has grown significantly in diverse security applications; nonetheless, the ability of the facial identification system to withstand an attack remains a significant concern. Research on non-invasive software-centric face spoofing detection systems have primarily focused on analyzing the luminance information present in face images, neglecting other components such as chroma and intensity which has the potential to be quite valuable for discriminating false faces from genuine ones. The proposed research work aims to utilize these components to find the differences or inconsistencies that could indicate the presence of a face spoofing attack. It is done by extracting and analyzing crucial information including energy, color distributions, and brightness of the color channels in an image. First, texture feature is extracted using Gray-Level Co-occurrence Matrix, GLCM. Second, the statistical descriptors features of RGB, HSV, YCbCr, CMYK, YIQ and YUV color channels are extracted to get the color distribution information. Subsequently, the integration of texture and color channel features results in the formation of a refined and enriched feature vector. At last, the feature vector is inputted into CNN architecture to classify the spoofed and genuine face images. The proposed method is assessed on NUAA Photograph Imposter dataset and has obtained a test accuracy of 99.88% in face spoof detection with HTER of 0.01%.