To date, BrainChip Studio utilized spiking neural networks to enable facial classification on partial faces.

This partial-face mode is useful in situations where the probe image or the extracted faces may be obscured due to hats, masks, scarves or camera angle.

BrainChip Studio 2018.3 uses a full-face mode to perform facial classifications. In situations where the entire face is visible in the probe image or in the extracted faces, this new mode provides a significant increase in facial classification accuracy.

Depending on the dataset used, testing indicates this mode provides a 10-30 percent improvement in accuracy, without impacting throughput.

BrainChip Studio’s facial classification technology works in environments where traditional biometric-based face recognition systems fail, including low-light, low-resolution, and visually-noisy environments.

BrainChip Studio is primarily used by law enforcement, intelligence, and counter-terrorism agencies that use existing CCTV infrastructure.

“We are always looking for ways to continually improve our products by listening to our customer requests,” says BrainChip’s Bob Beachler. “not surprisingly, improving accuracy is typically at the top of list for video analytic software. With BrainChip Studio 2018.3 we were able to provide a dramatic increase in accuracy.”