What Is Face Recognition?
A face recognition system uses computer vision technology to match a human face from a digital image or video frame against a database of faces. Deep learning algorithms are able to identify human faces in real-time using the video streams of digital surveillance cameras or webcams.
The performance of face recognition models using deep neural networks has drastically increased over the past five years. State-of-the-art algorithms achieve above human-level performance in Face recognition tasks (above 97.53%)
Features of Facial Recognition with Deep Learning
The frames of video cameras are analyzed to detect and localize human faces. Subsequently, facial recognition is applied to effectively identify the subject.
- Face verification is the process of comparing a face with another to verify if it is a match.
- Facial recognition refers to finding a face in an image database. The process is based on many iterations of face verification.
- Performing face recognition with camera streams of surveillance cameras.
- Edge AI allows high-performance applications of face recognition by combining on-device processing with cloud-based look-ups.
The Value of Face Recognition
Facial recognition is one of the most popular computer vision applications. Although the accuracy of facial recognition is still lower compared to iris recognition and fingerprint recognition, it is widely adopted due to its contactless process. The technology has made tremendous progress over the past few years with many AI face recognition models developed by Google, Facebook, and others achieving close to 99% accuracy.
- Automated and non-invasive identification of one or multiple people.
- Smart video surveillance for infrastructure protection in real-world settings.
- Accurate AI models with near-real-time performance that are increasingly robust to occlusions, expressions, illuminations, and pose.