![]() ![]() A variety of settings including backgrounds and low lighting for accurate performance in the real-world user environment.'Complex' hair detection tasks to train the neural network effectively on a relatively small initial dataset.Our hair segmentation neural network features: To address these issues, we assembled the balanced hair dataset and perform its ongoing training and improvement. The algorithms trained on studio-like photos will provide low quality results in real-world user surroundings with innaccuracy in hair makeover masks and hair color changer. Additionally, one needs to take into account the nature of the images. ![]() Large hair datasets are hard to obtain, while accurate hair recognition requires high quality, ground data annotations including different hairstyles, hair lengths, and environmental factors. virtual hair recoloring apps or hairstyle simulators. The algorithm produces a high-quality two-dimensional hair mask extracted from the input image that is well suited for AR applications, e.g. The core of our detection and segmentation technology is a neural network which returns a binary output, tagging the image pixels to human hair or the background. Banuba Hair Segmentation Before-After Demo How does hair recognition work? ![]()
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