Soil Condition Monitoring
Farmers can use technology to monitor soil conditions in order to use pesticides and fertilizers more efficiently and reduce negative impacts on the environment.
Revolutionizing Embedded Systems: Cutting-Edge Facial Recognition on MCUs
Face recognition technology has become a cornerstone of modern technology, widely applied in security surveillance, access control, and user authentication. Nuvoton Technology’s face recognition system, based on the NuMaker-M55M1 platform, integrates multiple core components, including image processing technology, TensorFlow Lite, Haar Cascade, and MobileFaceNet, achieving efficient and accurate recognition functionality while showcasing the powerful potential of embedded systems.
Nuvoton’s NuMaker-M55M1 serves as the operational environment, combining open-source tools and deep learning frameworks to construct the overall face recognition system. This system primarily consists of four core components: image processing technology for image preprocessing, TensorFlow Lite for model execution, Haar Cascade for face detection, and MobileFaceNet for feature extraction and matching. Image processing technology plays a foundational role by converting image data into formats usable by subsequent models. Through functional modules, it performs image preprocessing, feature extraction, and supports image and video input/output, format conversion, and feature matching. Based on this, image processing technology further detects facial regions in images, ensuring the input data required for subsequent analysis is of high quality and consistency.
Haar Cascade, based on Haar-like features, is a face detection technology that rapidly identifies facial regions in images through pre-trained classifiers. This method relies on layered feature classifiers for object detection, offering low computational demands and high speed, making it ideal for real-time applications. To enhance flexibility, image processing technology provides adjustable parameters, such as scaling factors and object size ranges, to balance detection speed and accuracy.
To achieve efficient operation of deep learning models, Nuvoton adopts TensorFlow Lite, a framework designed for mobile and embedded systems. This framework supports multiple programming languages, including C++ and Python, and effectively runs the MobileFaceNet model. In this system, TensorFlow Lite is used for neural network inference, ensuring stable operation on resource-constrained embedded devices.
MobileFaceNet, an enhanced version of MobileNet V2, is optimized for face recognition needs in embedded systems. The model extracts feature vectors from images and performs face matching using cosine similarity. Facial regions detected by image processing technology are converted by MobileFaceNet into feature vectors, which are then compared with registered features. If the similarity exceeds a preset threshold, the system considers it a match; otherwise, it is rejected.
The face recognition system based on NuMaker-M55M1 leverages the seamless integration of image processing technology, TensorFlow Lite, Haar Cascade, and MobileFaceNet to deliver efficient and reliable face detection and recognition. This solution not only meets the demands of high-security applications but also provides a promising approach for resource-limited embedded devices. Nuvoton Technology’s application design demonstrates the broad applicability and future potential of face recognition technology.
Figure 1. Face Enrollment
Figure 2. Face Recognition
