Facial Recognition Based on Converted Spiking Neural Network


Reference #: 01602

The University of South Carolina is offering licensing opportunities for Facial Recognition Based on Converted Spiking Neural Network


The current method for facial recognition requires a lot of computing resources (high-performance computers) which is not portable, and the accuracy is very limited.

Invention Description:

We developed computer software with an innovative method to recognize the same person from different pictures. So, we can identify who is who based on the photos or videos from different sources. The model we developed is a very efficient model that does not require a lot of computing resources, which can be loaded into mobile devices, such as cellphones, tablets, computing sticks, and mobile computing boards to make it portable. The accuracy of our model is equal to or even better than those existing model that requires a high-performance server or high-end GPU/CPU.

Potential Applications:

The potential market will be the first responders for security screening at the airports, ports, railroad/bus stations, shopping malls, etc., and similar highly populated areas.

Advantages and Benefits:

Our production requires significantly less computing power and battery power for achieving better recognition accuracy. The model we developed can be used to deploy to mobile devices for high portability.


For licensing information, contact:

Nikki Biagas, Licensing & Compliance Manager- bianik@sc.edu

UofSC Technology Commercialization Office- technology@sc.edu


Patent Information:
Software and Computing
For Information, Contact:
Technology Commercialization
University of South Carolina
Yu Qian
Youzhi Tang
deep learning
Facial recognition
mobile computing
Spiking neural networks
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