Boosting Face Presentation Attack Detection in Multi-Spectral Videos Through Score Fusion of Wavelet Partition Images

Presentation attack detection (PAD) algorithms have become an integral requirement for the secure usage of face recognition systems. As face recognition algorithms and applications increase from constrained to unconstrained environments and in multispectral scenarios, presentation attack detection algorithms must also increase their scope and effectiveness. It is important to realize that the PAD algorithms are not only effective for one environment or condition but rather be generalizable to a multitude of variabilities that are presented to a face recognition algorithm. With this motivation, as the first contribution, the article presents a unified PAD algorithm for different kinds of attacks such as printed photos, a replay of video, 3D masks, silicone masks, and wax faces. The proposed algorithm utilizes a combination of wavelet decomposed raw input images from sensor and face region data to detect whether the input image is bonafide or attacked. The second contribution of the article is the collection of a large presentation attack database in the NIR spectrum, containing images from individuals of two ethnicities. The database contains 500 print attack videos which comprise approximately 1,00,000 frames collectively in the NIR spectrum. Extensive evaluation of the algorithm on NIR images as well as visible spectrum images obtained from existing benchmark databases shows that the proposed algorithm yields state-of-the-art results and surpassed several complex and state-of-the-art algorithms. For instance, on benchmark datasets, namely CASIA-FASD, Replay-Attack, and MSU-MFSD, the proposed algorithm achieves a maximum error of 0.92% which is significantly lower than state-of-the-art attack detection algorithms

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

The Influence of Red Colouration on Human Perception of Aggression and Dominance in Neutral Settings

For both humans and nonhuman species, there is evidence that red colouration signals both emotional states (arousal/anger) and biological traits (dominance, health, and testosterone). The presence and intensity of red colouration correlates with male dominance and testosterone in a variety of animal species, and even artificial red stimuli can influence dominance interactions. Depending on the context in which it is perceived, red is associated with reward (e.g., mating) or avoidance of threat. Wearing red can therefore be advantageous in romantic or achievement contexts. It may also increase the probability of winning sporting contests. Both perceiver effects and wearer effects have been proposed as sources of enhanced winning chances for competitors wearing red in sporting competitions. We tested the hypothesis that artificial (clothing) colour can exploit the evolutionary associations between red and dominance/aggression and that this link is even detectable in neutral (non-competitive) settings. The first two experiments investigated whether a person wearing red was perceived as more aggressive/dominant than one wearing blue or grey. We detected a perceiver effect for red-wearers for perceptions of aggression, dominance, and anger that was independent of a wearer effect. This confirmed that the colour red may be a cue used to predict propensity for dominance and aggression in human males. We then explored differences in handgrip strength, self- and peer-assessed dominance, and actual dominant behaviour to test the hypothesis that red-wearers are physically and mentally stronger/more dominant than their blue-wearing opponents. Red-wearers were not stronger or perceived as more dominant or taller than blue-wearers, but we found some evidence that they may have acted more dominantly. However, in an online experiment rather than in a controlled laboratory setting, we found no wearer or perceiver effects on ratings of perceived dominance, height, or strength. Possible limitations of web-based approaches are discussed. Finally, we examined the consequences of allowing participants to choose from the full colour spectrum rather than forcing them to pick from only two or three clothing colours presented. When allowed to choose from the full spectrum, participants predominantly chose red shirts to make a person appear more aggressive or more dominant. There is some qualitative evidence for an “optimal red” in that participants’ choices clustered within a specific part of the red spectrum and no such clustering or colour preference was found for any of the control character traits. Overall, the results demonstrate that, in a laboratory setting, the colour red can have consistent effects on perceptions of aggression and dominance; this opens up a broad array of avenues for future work. These findings also have implications for non-academic contexts (e.g., whether wearing red can impact one’s performance in achievement contexts such as sporting contests or job interviews)