Face Recognition Performance Comparison of Fake Faces with Real Faces in Relation to Lighting

Abstract Face Recognition is widely used in security systems, such as surveillance, gate control systems, and guard robots, due to their user friendliness and convenience compared to other biometric approaches. Secure face recognition systems require advanced technology for face liveness detection, which can identify whether a face belongs to a real client or a portrait. However, with the development of display devices and technology, the tools and skills for carrying out spoofing attacks with images and videos have gradually evolved. In this paper, we compare real faces with high-definition facial videos from LED display devices, and present the changes in face recognition performance according to lighting direction

Machine Analysis of Facial Expressions

No abstract

Can we ID from CCTV? Image quality in digital CCTV and face identification performance

CCTV is used for an increasing number Of purposes, and the new generation of digital systems can be tailored to serve a wide range of security requirements. However, configuration decisions are often made without considering specific task requirements, e.g. the video quality needed for reliable person identification. Our Study investigated the relationship between video quality and the ability of untrained viewers to identify faces from digital CCTV images. The task required 80 participants to identify 64 faces belonging to 4 different ethnicities. Participants compared face images taken from a high quality photographs and low quality CCTV stills, which were recorded at 4 different video quality bit rates (32, 52, 72 and 92 Kbps). We found that the number of correct identifications decreased by 12 (similar to 18%) as MPEG-4 quality decreased from 92 to 32 Kbps, and by 4 (similar to 6%) as Wavelet video quality decreased from 92 to 32 Kbps. To achieve reliable and effective face identification, we recommend that MPEG-4 CCTV systems should be used over Wavelet, and video quality should not be lowered below 52 Kbps during video compression. We discuss the practical implications of these results for security, and contribute a contextual methodology for assessing CCTV video quality

A Noval Approach for Face Spoof Detection using Color-Texture, Distortion and Quality Parameters

Face spoof detection technique is used in many applications to check whether the given face is spoofed or not. It helps to detect the fake faces from genuine ones. An efficient proposed method for face spoofing detection is based on color-texture, image distortion and image quality parameters. The faces are detected from a compressed format image. The color-texture information from the luminance and chrominance channels extracted using Local Binary Pattern descriptor. The image distortion and image quality parameters are extracted from the same color space. The aim of this method is to bring together the advantages of these methods inorder to improve the accuracy of face spoofing detection. Multiclass SVM classifier is used to train each features of data and detect different face spoof attack. This paper describe a novel and appealing approach for detecting the fake faces from genuine ones using a color-texture combine with image distortion and image quality parameters. More importantly, the proposed method provides more accuracy, other than the method that described in the literature. It helps to separate the original face and fake face clearly and define the type of attack