Before the EPA, Utah saw 100 times more lead exposure. Breakthroughs, discoveries, and DIY tips sent six days a week. The evidence is clear--and in your hair. Americans were exposed to as much as 100 times more lead in their daily lives than they are today before the Environmental Protection Agency was established in 1970. In an effort to examine the dramatic reduction in toxic heavy metal exposure, researchers turned to human hair samples dating back a century.

This paper presents a novel non-invasive object classification approach using acoustic scattering, demonstrated through a case study on hair assessment. When an incident wave interacts with an object, it generates a scattered acoustic field encoding structural and material properties. By emitting acoustic stimuli and capturing the scattered signals from head-with-hair-sample objects, we classify hair type and moisture using AI-driven, deep-learning-based sound classification. We benchmark comprehensive methods, including (i) fully supervised deep learning, (ii) embedding-based classification, (iii) supervised foundation model fine-tuning, and (iv) self-supervised model fine-tuning. Our best strategy achieves nearly 90% classification accuracy by fine-tuning all parameters of a self-supervised model. These results highlight acoustic scattering as a privacy-preserving, non-contact alternative to visual classification, opening huge potential for applications in various industries.

Hair appearance is a complex phenomenon due to hair geometry and how the light bounces on different hair fibers. For this reason, reproducing a specific hair color in a rendering environment is a challenging task that requires manual work and expert knowledge in computer graphics to tune the result visually. While current hair capture methods focus on hair shape estimation many applications could benefit from an automated method for capturing the appearance of a physical hair sample, from augmented/virtual reality to hair dying development. Building on recent advances in inverse graphics and material capture using deep neural networks, we introduce a novel method for hair color digitization. Our proposed pipeline allows capturing the color appearance of a physical hair sample and renders synthetic images of hair with a similar appearance, simulating different hair styles and/or lighting environments. Since rendering realistic hair images requires path-tracing rendering, the conventional inverse graphics approach based on differentiable rendering is untractable. Our method is based on the combination of a controlled imaging device, a path-tracing renderer, and an inverse graphics model based on self-supervised machine learning, which does not require to use differentiable rendering to be trained. We illustrate the performance of our hair digitization method on both real and synthetic images and show that our approach can accurately capture and render hair color.

If you've watched enough reruns of shows like CSI, Bones, and Law and Order, you probably know by now that when forensic specialists find DNA evidence, the suspect is often identified within the next couple of minutes--as soon as the team sticks the results of DNA analysis into a computer program. Although the real life process isn't quite as speedy, DNA certainly has been the highest bar for identification in forensics. But when it comes to hair samples of missing persons or those found at crime scenes, sequencing the proteins in those locks may work better than DNA. In a study published in the journal PLOS One on September 7, researchers at Lawrence Livermore National Laboratory in California demonstrated a method of extracting genetic information from proteins found in hair that is remarkably reliable. "Currently forensic science is very dependent on DNA," says primary author Glendon Parker, a biochemist at Livermore.