Face Detection
Uses AI technology to detect human faces, even when wearing glasses.
A multi-context deep learning framework is established by applying deep learning CNN
models trained to integrate relevant inputs, including rPPG signals, motion and robust
luminance, to produce corresponding vital sign measurements.
Uses AI technology to detect human faces, even when wearing glasses.
Utilizing deep learning to adjust for motion and luminance variations.
Extracting waves by processing the subtle color fluctuations of the facial skin.
Remote Photoplethysmography (rPPG) uses reflected ambient light to measure subtle
changes under the skin of human face. It uses the color image of human face captured
by the camera to conduct independent component analysis (ICA) on RGB (Red, Green, Blue)
three-channel signals and restore the observed signal to a clean original signal.
FaceHeart recognizes the importance of accuracy. We use extensive data collected from clinical trials to calibrate our algorithms and achieve medical-grade accuracy.
Front Camera
15-30
Immobility / Fine movement
0~500cm or customized
Single / Multiple people
Edge computing (without WiFi)
Developing the AI-powered technology for monitoring physiological data in Taiwan, FaceHeart
seeks to expand globally, with various patents already been granted in the U.S., Europe and Asia.
In addition, FaceHeart aspires to be among the first set of Taiwanese companies
that receive FDA approval for software as a medical device (SaMD).
Facial recognition
and analysis
Image-based vital sign
measurement
Smart home systems and
other applications