Face blindness

Facial recognition is a complex task, often done immediately and readily, involving discrimination of subtle differences in facial structures with differences in facial expressions, ageing, perspectives and lighting. Facial recognition requires fast identification of stimuli which are then correlated against reservoirs of faces which are accumulated throughout life (Barton and Corrow, 2016). The facial recognition system is extremely complex, and if impaired, cannot be fully remedied by other areas of the brain. When such injury occurs early on in life, juvenile brain plasticity has been shown to be potentially inadequate to restore facial recognition functions, thereby suggesting that such an impairment can have severe, permanent implications, even at an early age (Barton et al., 2003) Damage to any part of the facial recognition mechanism may result in the development of face blindness. Such dysfunction results in the development of selective face-recognition and visual learning deficits, a condition called prosopagnosia. Prosopagnosia can be either acquired or congenital. The acquired form of prosopagnosia is considered to be a rare consequence of occipital or temporal lobe damage, possibly due to stroke or lesions occurring in adulthood. Congenital prosopagnosia, on the other hand, is usually not found associated with any gross abnormalities, and no clear underlying causative agent is found to be associated with the development of the disease (Grüter et al., 2008). Nevertheless, face blindness in children may also be associated with inherited or acquired brain lesions, and may not be exclusively of a congenital/hereditary aetiology. Moreover, prosopagnosia can also occur in association with other disorders, which may be psychiatric, developmental or associated with multiple types of visual impairment (Watson et al., 2016).peer-reviewe

Login Authentication with Facial Gesture Recognition

Facial recognition has proven to be very useful and versatile, from Facebook photo tagging and Snapchat filters to modeling fluid dynamics and designing for augmented reality. However, facial recognition has only been used for user login services in conjunction with expensive and restrictive hardware technologies, such as in smart phone devices like the iPhone x. This project aims to apply machine learning techniques to reliably distinguish user accounts with only common cameras to make facial recognition logins more accessible to website and software developers. To show the feasibility of this idea, we created a web API that recognizes a users face to log them in to their account, and we will create a simple website to test the reliability of our system. In this paper, we discuss our database-centric architecture model, use cases and activity diagrams, technologies we used for the website, API, and machine learning algorithms. We also provide the screenshots of our system, the user manual, and our future plan

Facial Recognition Technology: A Call for the Creation of a Framework Combining Government Regulation and a Commitment to Corporate Responsibility

At a fundamental level, the misuse of facial recognition endangers privacy, human rights, and constitutional rights. However, merely banning facial recognition will not address or solve the issues and risks inherent in the use of facial recognition. Rather than an outright ban, developing specific limitations controlling how or when facial recognition can be used in public or private spaces can better serve public interests. This paper suggests creating a framework that combines government regulation and a commitment to social responsibility by developers. Creating this multi-prong framework can help distribute the burden of regulating facial recognition technology amongst parties such as the government, the companies developing the technology, and the end-users. Finally, assessing the risk levels of different uses of facial recognition technology will further allow proper allocation and distribution of this burden amongst the parties

Privacy-Preserving Facial Recognition Using Biometric-Capsules

Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design. In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods

Countenancing Employment Discrimination: Facial Recognition in Background Checks

Employing facial recognition technology implicates anti-discrimination law under Title VII of the Civil Rights Act when used as a factor in employment decisions. The very technological breakthroughs that made facial recognition technology commercially viable—data compression and artificial intelligence— also contribute to making facial recognition technology discriminatory in its effect on members of classes protected by Title VII. This Article first explains how facial recognition technology works and its application in employee background checks. Then, it analyzes whether the use of facial recognition technology in background checks violates Title VII under the disparate impact theory of liability due to the known issue of skewed data sets and disproportionate inaccuracy on some populations. The Article concludes by calling on the Equal Employment Opportunity Commission to issue specific guidance warning employers of impending liability under Title VII, including class action liability, due to the use of facial recognition technology, and to use its enforcement authority to file lawsuits against employers who continue to use the technology

Review of Facial Recognition and Liveness Detect

Facial recognition technology has been dramatically integrated into almost all the aspects of human life, such as mobile payment, identification applications, security management, and criminal cases, etc. However, these applications can be easily fooled by deliberate spoofing strategies. To ensure the identifications of users and avoid being spoofed are the central cores of this technology. As a result, its safeness and accuracy issues attract researchers to dig into this field. In terms of present existing deception and spoofing strategies, liveness detection plays a significant role in improving the robustness of facial recognition techniques. This paper will summarize the current mainstream facial recognition technology methods. The basic ideas, methods, implementations, and corresponding drawbacks of current facial recognition methods are in this paper. The future trends of facial recognition and liveness detection are also discussed and concluded

Impact assessment of facial recognition algorithms\u27 performance when modifying nose dimensions

This work quantitatively measures the impact of modifying the nasal width and length dimensions, in a simulated plastic surgery, on the Facial Recognition algorithms, Principal Component Analysis (PCA), Linear Discrimination Analysis (LDA), and Local Binary Patterns Histogram (LBPH). This was integrated through the use of OpenCV. It was found that as the nose width increases beyond 40% its original width, there is an average decrease in facial recognition performance of up to 14%. It was also found that as the nose was modified vertically, there was less than a 3% decrease in performance for the facial recognition algorithms. These rates are consistent with previous research in the field although, these are more quantitative. The experimental structure used is modular in nature and allows for easy insertion of other Facial Recognition Algorithms and other Facial Recognition Datasets