Synergylabs has been providing ai solutions to indian market since 2016. our team has 20+ researchers and developers and has 100+ years of cumulative experience in the field of artificial intelligence. facial recognition has proven to be an effective tool in security systems across the world and at synergy labs we are using this technology to create safe learning, living and working environments for people. synergy labs has developed its facial recognition system (frs) that not only matches faces but also tracks individuals in their environment, while collecting and detecting pose-estimation and expressions-estimation in the-wild videos. based on deep learning models, the frs detects human faces within images and videos, and identifies people. customer benefits : robust and challenging than conventional methods contactless biometric identification hardware available at competitive prices to process videos in realtime hassle-free and better than the iris recognition or finger print biometric systems capable of replacing the wide spread use of physical photo ids tracking and identifying 100+ individuals simultaneously and all the processing is done on the edge key features : • real-time input video feed. • identifying and tracking individuals across the frames using combination of deep neural network and computer vision algorithms to track people with high accuracy. • detecting faces using hogs-keeping structure and temporal (across video frames) information for better tracking and tagging. • finger printing face for race, gender and other key parameters and heuristically matching them with other video frame for more robust tracking. • shadow normalisation for better accuracy in open spaces. •matching faces using deep neural network with known set of people. performance : •works both on and off the shelf hardware (camera, cpu and gpu) or custom-built hardware for better performance and accuracy. •mobile net ssd based neural networks to identify and track people with enhanced accuracy. •deep convolutional neural networks trained on synthetic data saves significant resources on data collection and processing while retaining high quality results.
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