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#facialrecognition

bryontaylor@sysad.org
bryontaylor

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Imagine if you could walk into your local Walmart and your favorite products were delivered to you before you even had to ask. Imagine the possibilities if every camera in your town was connected, and had facial recognition software that could identify you, and call up your preferences on screen before you even asked for it. Facial recognition use cases in retail are endless. Facial Recognition Use Cases in Retail

#biometrics #personalization #selfservice #retail #technology #iris #eyetracking #retailing #facialrecognition #facerecognition #facedetection #computervision

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garryknight@diasp.org
Garry Knight

**[2109.06467] Dodging Attack Using Carefully Crafted Natural Makeup | Cornell University.

In this study, we present a novel black-box AML attack which carefully crafts natural makeup, which, when applied on a human participant, prevents the participant from being identified by facial recognition models.

https://arxiv.org/abs/2109.06467

As [Slashdot](shttps://yro.slashdot.org/story/21/09/17/2113256/researchers-defeated-advanced-facial-recognition-tech-using-makeup) explains:

Researchers have found a new and surprisingly simple method for bypassing facial recognition software using makeup patterns. A new study from Ben-Gurion University of the Negev found that software-generated makeup patterns can be used to consistently bypass state-of-the-art facial recognition software, with digitally and physically-applied makeup fooling some systems with a success rate as high as 98 percent. In their experiment, the researchers defined their 20 participants as blacklisted individuals so their identification would be flagged by the system. They then used a selfie app called YouCam Makeup to digitally apply makeup to the facial images according to the heatmap which targets the most identifiable regions of the face. A makeup artist then emulated the digital makeup onto the participants using natural-looking makeup in order to test the target model's ability to identify them in a realistic situation.

The researchers tested the attack method in a simulated real-world scenario in which participants wearing the makeup walked through a hallway to see whether they would be detected by a facial recognition system. The hallway was equipped with two live cameras that streamed to the MTCNN face detector while evaluating the system's ability to identify the participant. The experiment saw 100 percent success in the digital experiments on both the FaceNet model and the LResNet model, according to the paper. In the physical experiments, the participants were detected in 47.6 percent of the frames if they weren't wearing any makeup and 33.7 percent of the frames if they wore randomly applied makeup. Using the researchers' method of applying makeup to the highly identifiable parts of the attacker's face, they were only recognized in 1.2 percent of the frames.

#technology #tech #security #privacy #FacialRecognition

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