Face Liveness

In face liveness classification, obfuscation is a malevolent technique, which allows concealing a culprit’s true identity from a biometric recognition system based on both passive or active liveness detection. In essence, obfuscation is somewhat close to Presentation Attacks (PAs)

Liveness detection challenges are public competitions organized to expose vulnerabilities of the biometric systems, attest the most promising algorithms and models, adapt them to hostile and uncontrolled environments, as well as introduce training and testing datasets. Facial

Occlusion of a certain anatomic trait is a recurring problem in biometrics. While it drew mainstream attention only in 2020 with the onset of face masks, the issue has been addressed since at least 2009 by Hazım Kemal Ekenel & Rainer Stiefelhagen who explored lower

Facial recognition is one of the earliest techniques used to identify a person using Artificial Intelligence (AI). The first instance can be dated back to 1964 when Woodrow Bledsoe conducted a series of experiments using a primitive scanner to capture a human face and

Although facial recognition has shown steady improvement in accuracy and liveness detection — both passive and active liveness — there is still a lack of inclusion of all racial and gender characteristics and the issue is still addressed in scientific publications occasionally. A number

The human retina is a thin layer of tissue with intricate blood vessels located at the back of the eye. A part of face liveness detection, retinal recognition focuses on the patterns of these blood vessels which are reported to be highly unique and stable at the same time. Retinal

Aging is a natural physiological process that occurs in a human organism during its growth. Face is a region where aging effects are more apparent when compared to rest of the body. Apart from natural growth, the facial structure can also be impacted by age-related weight

Plastic surgery is a medically induced face alteration, which allows to both enhance aesthetic nuances of one’s appearance or correct gained/innate defects, including the craniofacial anomalies: cleft lip, hemifacial microsomia, and others. According to Statista, 4,667,931 plastic

In face anti spoofing, makeup presentation attack (M-PA) is a type of presentation attack, in which perpetrators use non-permanent facial alterations to bypass a security system based on facial recognition. The purpose of this attack is either to gain authorization into a system as

In face liveness, iris recognition is a biometric modality and automatic verification technique that allows identifying a person by the unique parameters — patterns, coloration — of their iris. The first iris recognition algorithm was postulated in the early 90s by John Daugman, which

Iris recognition, thought not a fully passive liveness detection system, became commercialized in 1999 when LG released the first iris scanner IrisAccess 2200 meant for broad usage. However, iris anti-spoofing issues were barely addressed at the time. In 2005, the first iris recognition

A face antispoofing database represents a collection of facial images used for training and testing existing detection solutions. These datasets range in sample variety, visual quality, digital face manipulation methods applied to them, types of Presentation Attacks (PAs)

Before iris anti-spoofing, there was an early concept of iris recognition that can be attributed to a 1953 clinical textbook Physiology of the Eye by F. H. Adler. The book stated that iris photographs could successfully replace fingerprints in terms of personal identification due

Facial morphing is a technique that blends or morphs multiple faces into a single image. This face manipulation technique is widely used by malicious actors to bypass security systems for accessing restricted areas, protected data, illegally crossing state borders, and so on

Active facial liveness detection rivals the passive detection type, but many experts see it as an obsolete security measure. Face recognition systems have been widely used since at least 2002: during the Super Bowl, this technology was put to a test by law enforcement agencies (even

Passive facial liveness detection is a “second generation” solution, which does not require a person to perform a task to get verified. The challenge of differentiating between human-like behavior from actual human behavior has been studied for decades. In 1950 Alan Turing

Machine-operated systems are quicker and more productive at performing repetitive tasks. Facial anti-spoofing with its types, countermeasures, and challenges, is exactly a system, where a vast number of faces should be recognized instantly. For instance, American Airlines

Face recognition has a wide range of applications: from shopping and entertainment to law enforcement, banking, and surveillance. When used as a tool for biometrical recognition, it should retain a high level of accuracy. This in turn requires integration of a number of highly

There are two major certifications for face liveness detection and facial antispoofing, introduced since 2017. Both certifications — constituted by FIDO and ISO — are valid on par. FIDO also incorporates some clauses and rules from the ISO face antispoofing standard