Spoofing Faces Using Makeup: An Investigative Study

International audienceMakeup can be used to alter the facial appearance of a person. Previous studies have established the potential of using makeup to obfuscate the identity of an individual with respect to an automated face matcher. In this work, we analyze the potential of using makeup for spoofing an identity, where an individual attempts to impersonate another per-son's facial appearance. In this regard, we first assemble a set of face images downloaded from the internet where individuals use facial cosmetics to impersonate celebrities. We next determine the impact of this alteration on two different face matchers. Experiments suggest that automated face matchers are vulnerable to makeup-induced spoofing and that the success of spoofing is impacted by the appearance of the impersonator's face and the target face being spoofed. Further, an identification experiment is conducted to show that the spoofed faces are successfully matched at better ranks after the application of makeup. To the best of our knowledge, this is the first work that systematically studies the impact of makeup-induced face spoofing on automated face recognition

Facial Image Analysis for Body Mass Index, Makeup and Identity

The principal aim of facial image analysis in computer vision is to extract valuable information(e.g., age, gender, ethnicity, and identity) by interpreting perceived electronic signals from face images. In this dissertation, we develop facial image analysis systems for body mass index (BMI) prediction, makeup detection, as well as facial identity with makeup changes and BMI variations.;BMI is a commonly used measure of body fatness. In the first part of this thesis, we study BMI related topics. At first, we develop a computational method to predict BMI information from face images automatically. We formulate the BMI prediction from facial features as a machine vision problem. Three regression methods, including least square estimation, Gaussian processes for regression, and support vector regression are employed to predict the BMI value. Our preliminary results show that it is feasible to develop a computational system for BMI prediction from face images. Secondly, we address the influence of BMI changes on face identity. Both synthesized and real face images are assembled as the databases to facilitate our study. Empirically, we found that large BMI alterations can significantly reduce the matching accuracy of the face recognition system. Then we study if the influence of BMI changes can be reduced to improve the face recognition performance. The partial least squares (PLS) method is applied for this purpose. Experimental results show the feasibility to develop algorithms to address the influence of facial adiposity variations on face recognition, caused by BMI changes.;Makeup can affect facial appearance obviously. In the second part of this thesis, we deal with makeup influence on face identity. It is principal to perform makeup detection at first to address makeup influence. Four categories of features are proposed to characterize facial makeup cues in our study, including skin color tone, skin smoothness, texture, and highlight. A patch selection scheme and discriminative mapping are presented to enhance the performance of makeup detection. Secondly, we study dual attributes from makeup and non-makeup faces separately to reflect facial appearance changes caused by makeup in a semantic level. Cross-makeup attribute classification and accuracy change analysis is operated to divide dual attributes into four categories according to different makeup effects. To develop a face recognition system that is robust to facial makeup, PLS method is proposed on features extracted from local patches. We also propose a dual-attributes based method for face verification. Shared dual attributes can be used to measure facial similarity, rather than a direct matching with low-level features. Experimental results demonstrate the feasibility to eliminate the influence of makeup on face recognition.;In summary, contributions of this dissertation center in developing facial image analysis systems to deal with newly emerged topics effectively, i.e., BMI prediction, makeup detection, and the rcognition of face identity with makeup and BMI changes. In particular,to the best of our knowledge, BMI related topics, i.e., BMI prediction; the influence of BMI changes on face recognition; and face recognition robust to BMI changes are first explorations to the biometrics society

Human age estimation performance based on facial images: Potential implications for refugee processing

This item is only available electronically.Government agencies responsible for refugee processing are often challenged to develop an accurate identification profile for asylum seekers when reliable documentation is unavailable. As processing and support systems are designed differently for children and adults, it is critical to determine an accurate age estimate. However, current methodologies to estimate an individual’s age lack accuracy, verification or violate ethical standards, presenting the need to explore an alternative age estimation procedure. The present study aimed to explore human performance of age estimation, using facial images, for refugee processing purposes. As a within-subjects design, participants (N = 46) undertook a perceptual task to estimate the age of both children (11–17 years) and adults (18–24 years) from a facial image. Estimates were more accurate (i.e., closer to the true age) for children than for adults, although there was a consistent tendency to overestimate the true age. If this methodology was utilised for refugee processing, trends of over-, as opposed to under-estimation, increase the likelihood of a child being incorrectly labelled as an adult, than an adult labelled as a child. Future research could aim to further develop a task performance baseline by defining group-specific biases of estimators and estimations. The results of this study provide government agencies with an initial understanding of task performance and the potential biases in human perception.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 201

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