FACE BIOMETRIC ANTI-SPOOFING

  ABSTRACT

Despite a great deal of progress in face recognition, current systems are vulnerable to spoofing attacks. Several anti-spoofing methods have been proposed to determine whether there is a live person or an artificial replica in front of the camera of the face recognition system. Yet, developing efficient protection methods against this threat has proven to be a challenging task. In this chapter, we present a comprehensive overview of the state-of-the-art in face spoofing and anti-spoofing, describing existing methodologies, their pros, and cons, results, and databases. Moreover, after a comprehensive review of the available literature in the field, we present a new face anti-spoofing method based on color texture analysis, which analyzes the joint color-texture information from the luminance and the chrominance channels using color local binary patterns descriptor. The experiments on two challenging spoofing database exhibited excellent results. In particular, in inter-database evaluation, the proposed approach showed very promising generalization capabilities. We hope this case study simulates the development of generalized face liveliness detection. Lastly, we point out some of the potential research directions in face anti-spoofing.

CHAPTER 1

INTRODUCTION

Over the past decade, automated face recognition systems have been adopted in various applications because of the face’s rich features that offer a strong biometric cue for recognizing individuals. In fact, facial recognition systems are already being used on a large scale. For instance, the UI DAI program provides identity to all person’s residents in India using face, and Microsoft Kinect employs face recognition to access the dashboard and automatic sign-in to the Xbox Live profile.

Likewise, face bio-metrics based access control is a ubiquitous feature available now on mobile devices as an alternative to passwords, e.g., Android Kit Kat mobile OS, Lenovo VeriFone, Asus Smart-Logon, and Toshiba Smart Face. Since the deployment of face recognition systems is growing year after year, people are also becoming more familiar with their use in daily life. Consequently, the security weakness of face recognition systems is getting better known to the general public.

However, vulnerabilities of face systems to attacks are mainly overlooked, even as a registered user and thereby gaining illegitimate access and advantages. Face spoofing attack is also known as “direct attack” or “presentation when it is not difficult nowadays to find websites or even tutorial videos giving detailed guidance on how to attack face systems to gain unauthorized access.

In particular, existing systems are vulnerable to facial spoofing attacks. Facial spoof attack is a process in which a fraudulent user can subvert or attack a face recognition system by the masquerading attack”. Face spoofing is also a major issue for companies selling face Biometric-based identity management solutions.

CHAPTER-2

HISTORY OF FACE BIOMETRICS

Until recently, face recognition technology was commonly viewed as something straight out of science fiction. But over the past decade, this groundbreaking technology has not just become viable, it has become widespread. In fact, it’s difficult to read technology news these days without seeing something about face recognition.

There are several industries benefitting from this technology. Law enforcement agencies are using face recognition to keep communities safer. Retailers are preventing crime and violence. Airports are improving the traveler’s convenience and security. And mobile phone companies are using face recognition to provide consumers with new layers of biometric security.

It may seem to some that facial recognition came out of nowhere. But in truth, this technology has been in the works for some time. This post will take a look at the history of face recognition in order to shed light on how this transformative tech came to be, and how it has evolved over time.

MANUAL MEASUREMENTS BY BLEDSOE (1960)

Many would say that the father of facial recognition was Woodrow Wilson Bledsoe. Working in the 1960s, Bledsoe developed a system that could classify photos of faces by hand using what’s known as a RAND tablet, a device that people could use to input horizontal and vertical coordinates on a grid using a stylus that emitted electromagnetic pulses. The system could be used to manually record the coordinate locations of various facial features including the eyes, nose, hairline, and mouth.

CHAPTER-3

BENEFITS OF FACE BIOMETRICS

Benefits of Facial Recognition:

Security improved retail shopping and banking, and more. The benefits of facial recognition technology and its cons are controversial issues. Many stakeholders are pointing out the pros, but there are also detractors voicing its disadvantages. There are many concerns around face recognition technology, such as the invasion of privacy, abuse of power, what rogue elements within government agencies could do with it, and more. Already, the heated debate around facial recognition has caused some public relations backlashes. As a result, investors could stay clear of the technology, inhibiting its development. Still, many stakeholders point out that with regulation, facial recognition has excellent potential. Some of the benefits of facial recognition technology are better.

CHAPTER-4

FACE SPOOFING AND ANTI-SPOOFING

4. 1 FACE SPOOFING:

Despite a great deal of progress in face recognition systems, face spoofing still poses a serious threat. Most of the existing academic and commercial facial recognition systems may be spoofed by Akhtar and Akhtar et al. a photo of a genuine user a video of a genuine user a 3D face model (mask) of a genuine user; a reverse-engineered face image from the template of a genuine user; a sketch of a genuine user; an impostor wearing specific make- up to look like a genuine user; an impostor who underwent plastic surgery to look like a genuine user; a photo or a video, generated using computer graphics, of a genuine user. The easiest and most common face spoofing attack is to submit a photograph or video of a legitimate user to the face recognition systems.

4.1.1 FACE ANTI-SPOOFING:

The pioneer studies on biometric vulnerabilities, such as, highlighted the necessity of developing efficient protection schemes against face spoofing attacks.

4.2 TYPES OF ATTACKS

4.2.1 Generalization to Unknown Attacks:

Many visual cues for non-intrusive spoofing detection have been already explored and impressive results have been reported on individual databases. However, the varying nature of spoofing attacks and acquisition conditions makes it impossible to predict how single anti-spoofing techniques, e.g. facial texture analysis can generalize the problem in real-world applications.

Moreover, we cannot foresee all possible attack scenarios and cover them in databases because the imagination of the human mind always finds out new tricks to fool existing systems. As one obviously cannot foresee all possible types of fake faces, a one-class approach modeling only the genuine facial texture distribution could be a promising direction. This has been successfully applied in voice anti-spoofing, for instance.

4.2.2  Fusion of Countermeasures:

It is reasonable to assume that no single superior technique is able to detect all known, let alone unseen, spoofing attacks. Therefore, the problem of spoofing attacks should be broken down into attack-specific subproblems that are solvable if a proper combination of complementary countermeasures is used. In this manner, a network of attack-specific spoofing detectors could be used to construct a flexible anti-spoofing framework in which new techniques can be easily integrated to patch the existing vulnerabilities in no time when new countermeasures appear. This obviously raises the problem of fusing different spoofing countermeasures which has not been studied much besides the algorithms proposed within the context of the IJCB 2011 competition on countermeasures to 2D facial spoofing attacks.

4.2.3 Biometric System and Countermeasures:

A spoofing countermeasure is usually not designated to operate as a stand-alone procedure but in a joint operation with a recognition system. However, most works on anti-spoofing tend to focus only on the spoofing detection part hence omitting to integrate the countermeasure into a recognition system. In practice, integrating the countermeasure will affect the performance of the recognition system. While it will reduce its vulnerability to spoofing attacks, it may also decrease the recognition performance. The open issue is how to combine the spoofing countermeasure and the biometric recognition so that the combined biometric recognition system is robust to spoofing and does not suffer from reduced recognition accuracy.

CHAPTER-5

TYPES OF APPROACHES

5.1 Challenge-Response Approach:

Liveness and motion analysis based spoofing detection is rather difficult to perform by observing only spontaneous facial motion during short video sequences. This problem can be simplified by prompting the user to do some specific random action or challenge (such as smiling and moving the head to the right). The user’s response (if any) will provide liveness evidence. This is called a challenge-response approach for spoofing detection. The drawback of such an approach is that it requires user cooperation, thus making the authentication process a time-consuming. Another advantage of non-intrusive techniques is that from challenge-response based countermeasures it is rather easy to deduce which liveness cues need to be fooled. For instance, the request for uttering words suggests that analysis of synchronized lip movement and lip readied.

5.2 Contextual Information:

Face images captured from face spoofs may visually look very similar to the images captured from live faces. Thus, face spoofing detection may be difficult to perform based on only a single face image or a relatively short video sequence. Depending on the imaging and fake face quality, it is nearly impossible, even for humans, to tell the difference between a genuine face and a fake one without any scene information or unnatural motion or facial texture patterns. However, we can immediately notice if there is something suspicious in the view, e.g. if someone is holding a video display or a photograph in front of the camera. Therefore, scenic cues can be exploited for determining whether display medium is present in the observed scene as shown in the Fig.5.2

CHAPTER-6

ADVANTAGES

• Security through Biometric Authentication: One of the benefits of facial recognition system centers on its application in biometrics. It can be used as a part of identification and access control systems in organizations, as well as personal devices, such as in the case of smart phones.

• Automated Image Recognition: The system can also be used to enable automated image recognition capabilities. Consider Facebook as an example. Through machine learning and Big Data analytics, the social networking site can recognize photos of its users and allow automated linking or tagging to individual user profiles.

• Deployment in Security Measures: Similar to biometric application and automated image recognition, another advantage of facial recognition system involves its application in law enforcement and security systems. Automated biometric identity allows less intrusive monitoring and mass identification.

DRAWBACKS

• Issues about Reliability and Efficiency: A notable disadvantage of facial recognition system is that it is less reliable and efficient than other biometric systems such as fingerprint. Factors such as illumination, expression, and image or video quality, as well as software and hardware capabilities, can affect the performance of the system.

• Further Reports about It Reliability: Several reports have pointed out the in effectiveness of some systems. For example, a report by an advocacy organization noted that the systems used by law enforcement agencies in the U.K. had an accuracy rate of only 2 percent. Applications in London and Tampa, Florida did not result in better law enforcement according to another report.

CHAPTER-7

APPLICATIONS

7.1 Payments:

It doesn’t take a genius to work out why businesses want payments to be easy. Online shopping and contact less cards are just two examples that demonstrate the seamlessness of postmodern purchases. With Face Tech, however, customers wouldn’t even need their cards.

7.2 Access and security:

As well as verifying a payment, facial biometrics can be integrated with physical devices and objects. Instead of using passcodes, mobile phones and other consumer electronics will be accessed via owner’s facial features.

7.3 Criminal identification:

If Face Tech can be used to keep unauthorized people out of facilities, surely it can be used to help put them firmly inside them. This is exactly what the US Federal Bureau of Investigation is attempting to do by using a machine learning algorithm to identify suspects from their driver’s licenses.

7.4 Advertising:

The ability to collect and collate masses of personal data has given marketers and advertisers the chance to get closer than ever to their target markets. Face Tech could do much the same, by allowing companies to recognize certain demographics–for instance, if the customer is a male between the ages of 12 and 21.

CHAPTER-8

CONCLUSION

The facial expression recognition system presented in this research work contributes a resilient face recognition model based on the mapping of behavioral characteristics with the physiological biometric characteristics. The physiological characteristics of the human face with relevance to various expressions such as happiness, sadness, fear, anger, surprise and disgust are associated with geometrical structures which restored as base matching template for the recognition system.

The design of a novel asymmetric cryptosystem based on biometrics having features like hierarchical group security eliminates the use of passwords and smart cards as opposed to earlier cryptosystems. It requires a special hardware support like all other biometrics system.

This research work promises a new direction of research in the field of asymmetric biometric cryptosystems which is highly desirable in order to get rid of passwords and smartcards completely. Experimental analysis and study show that the hierarchical security structures are effective in geometric shape identification for physiological traits.

FUTURE OUTLOOK

The use of spherical canonical images allows us to perform matching in the spherical harmonic transform domain, which does not require preliminary alignment of the images. The errors introduced by embedding in to an expressional space with some predefined geometry are avoided.

In this facial expression recognition setup, end-to-end processing comprises the face surface acquisition and reconstruction, smoothening, sub sampling to approximately 2500 points. Facial surface cropping measurement of large positions of distances between all the points using a parallelized parametric version is utilized.

The general experimental evaluation of the face expressional system guarantees better face recognition rates. Having examined techniques to cope with expression variation, in future it may be investigated in more depth about the face classification problem and optimal fusion of color and depth information.

Further study can be laid down in the direction of allele of gene matching to the geometric factors of the facial expressions. The genetic property evolution frame work for facial expressional system can be studied to suit the requirement of different security models such as criminal detection, governmental confidential security breaches etc.