Face Recognition on the Edge - Reduce Cost, Complexity and Power Consumption of Your Design
The market for facial recognition is expected to double by 2024 to $7B through the adoption of various training models and inference engines. As with any new technology, there is a learning curve on how to bring a technology to a platform, while ensuring you are optimizing performance, security, and time to market. Facial recognition is the same, but also brings in added questions of privacy for the consumer.
In this webinar we will introduce NXP’s MCU-based SLN-VIZN-IOT solution which provides OEMs with a fully integrated, self-contained, software and hardware solution. This includes the i.MX RT106F and pre- integrated machine learning face recognition algorithms within the NXP i.MX RT run-time library, as well as all required drivers for peripherals, such as camera and memory drivers, all running on FreeRTOS. This cost-effective, easy to use face recognition implementation facilitates the demand for a face-based Friction Free Interface that can be embedded in a variety of products across home, commercial, and industrial applications, thus eliminating the need to use hard to learn and time- consuming mechanisms to identify users.
Attendees will learn:
- How to significantly reduce the cost, complexity, and power consumption of a face recognition design by using an MCU-based solution
- Privacy benefits of an offline face recognition solution
- What a "solution" is and how it can reduce time-to-market for product-designers
- Overview of the i.MX RT106F solution for local (offline) face and emotion recognition