22 research outputs found
Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation
Swift response to the detection of endangered minors is an ongoing concern
for law enforcement. Many child-focused investigations hinge on digital
evidence discovery and analysis. Automated age estimation techniques are needed
to aid in these investigations to expedite this evidence discovery process, and
decrease investigator exposure to traumatic material. Automated techniques also
show promise in decreasing the overflowing backlog of evidence obtained from
increasing numbers of devices and online services. A lack of sufficient
training data combined with natural human variance has been long hindering
accurate automated age estimation -- especially for underage subjects. This
paper presented a comprehensive evaluation of the performance of two cloud age
estimation services (Amazon Web Service's Rekognition service and Microsoft
Azure's Face API) against a dataset of over 21,800 underage subjects. The
objective of this work is to evaluate the influence that certain human
biometric factors, facial expressions, and image quality (i.e. blur, noise,
exposure and resolution) have on the outcome of automated age estimation
services. A thorough evaluation allows us to identify the most influential
factors to be overcome in future age estimation systems
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