On the Robustness of Face Recognition Algorithms Against Attacks and Bias

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.Comment: Accepted in Senior Member Track, AAAI202

Digital Eye Modification A Countermeasure to Automated Face Recognition

This thesis describes and assesses a series of subtle digital eye modification techniques and their impact on automated face detection and recognition. The techniques involve altering the relative positioning of a person\u27s eyes in a photograph using a variety of horizontal and vertical movements local to the eye regions. Testing with Eigenfaces, Fisherfaces, and Circular Local Binary Pattern face recognition algorithms on a database of 40 subjects and over 4000 modified images shows these subtle geometric changes to the eyes can degrade automated face recognition accuracy by 40% or more. Certain modifications even lower the chance a face is detected at all by about 20%. The combined effect of particular eye modifications resulted in subjects being both detected and recognized less than 20% of time. These results indicate that nearly imperceptible modifications made to one or more key facial features may foil face recognition algorithms

The effects of scarring on face recognition

The focus of this research is the effects of scarring on face recognition. Face recognition is a common biometric modality implemented for access control operations such as customs and borders. The recent report from the Special Group on Issues Affecting Facial Recognition and Best Practices for their Mitigation highlighted scarring as one of the emerging challenges. The significance of this problem extends to the ISO/IEC and national agencies are researching to enhance their intelligence capabilities. Data was collected on face images with and without scars, using theatrical special effects to simulate scarring on the face and also from subjects that have developed scarring within their lifetime. A total of 60 subjects participated in this data collection, 30 without scarring of any kind and 30 with preexisting scars. Controlled data on scarring is problematic for face recognition research as scarring has various manifestations among individuals, yet is universal in that all individuals will manifest some degree of scarring. Effect analysis was done with controlled scarring to observe the factor alone, and wild scarring that is encountered during operations for realistic contextualization. Two environments were included in this study, a controlled studio that represented an ideal face capture setting and a mock border control booth simulating an operational use case

Impact and Detection of Facial Beautification in Face Recognition: An Overview

International audienceFacial beautification induced by plastic surgery, cosmetics or retouching has the ability to substantially alter the appearance of face images. Such types of beautification can negatively affect the accuracy of face recognition systems. In this work, a conceptual categorisation of beautification is presented, relevant scenarios with respect to face recognition are discussed, and related publications are revisited. Additionally, technical considerations and trade-offs of the surveyed methods are summarized along with open issues and challenges in the field. This survey is targeted to provide a comprehensive point of reference for biometric researchers and practitioners working in the field of face recognition, who aim at tackling challenges caused by facial beautification

Computer analysis of face beauty: a survey

The human face conveys to other human beings, and potentially to computer systems, information such as identity, intentions, emotional and health states, attractiveness, age, gender and ethnicity. In most cases analyzing this information involves the computer science as well as the human and medical sciences. The most studied multidisciplinary problems are analyzing emotions, estimating age and modeling aging effects. An emerging area is the analysis of human attractiveness. The purpose of this paper is to survey recent research on the computer analysis of human beauty. First we present results in human sciences and medicine pointing to a largely shared and data-driven perception of attractiveness, which is a rationale of computer beauty analysis. After discussing practical application areas, we survey current studies on the automatic analysis of facial attractiveness aimed at: i) relating attractiveness to particular facial features; ii) assessing attractiveness automatically; iii) improving the attractiveness of 2D or 3D face images. Finally we discuss open problems and possible lines of research

Call Me Caitlyn: Making and making over the 'authentic' transgender body in Anglo-American popular culture

A conception of transgender identity as an ‘authentic’ gendered core ‘trapped’ within a mismatched corporeality, and made tangible through corporeal transformations, has attained unprecedented legibility in contemporary Anglo-American media. Whilst pop-cultural articulations of this discourse have received some scholarly attention, the question of why this 'wrong body' paradigm has solidified as the normative explanation for gender transition within the popular media remains underexplored. This paper argues that this discourse has attained cultural pre-eminence through its convergence with a broader media and commercial zeitgeist, in which corporeal alteration and maintenance are perceived as means of accessing one’s ‘authentic’ self. I analyse the media representations of two transgender celebrities: Caitlyn Jenner and Nadia Almada, alongside the reality TV show TRANSform Me, exploring how these women’s gender transitions have been discursively aligned with a cultural imperative for all women, cisgender or trans, to display their authentic femininity through bodily work. This demonstrates how established tropes of authenticity-via-bodily transformation, have enabled transgender to become culturally legible through the wrong body trope. Problematically, I argue, this process has worked to demarcate ideals of ‘acceptable’ transgender subjectivity: self-sufficient, normatively feminine, and eager to embrace the possibilities for happiness and social integration provided by the commercial domain

Fashioning Seoul: Everyday Practices of Dress in the Korean Wave

Senior Project submitted to The Division of Social Studies of Bard College