Liveness Detection (Anti-Spoofing) Technology
Liveness Detection (Anti-Spoofing) Technology
Eliminate photo-based hacking of facial recognition. Liveness Detection (Anti-Spoofing) Technology determines whether the face in front of the camera is a real person or a spoof.
Built on technology jointly patented with Panasonic, our Liveness Detection (Anti-Spoofing) Technology is a biometric security core technology that prevents spoofing and hacking in systems such as facial recognition and eKYC (online identity verification). It works with visible-light cameras such as USB webcams and built-in PC cameras, and delivers high-accuracy determination using deep learning. It prevents spoofing through "Fake Face Detection," which can determine liveness from a single frame, and "Action Detection," which uses time-series data to detect specified actions such as blinking and changes in face orientation. By compressing the learning model, we achieve a processing speed of approximately 100 ms per frame even on CPU alone (measured on our Windows machine with an 8th-generation Core i7 CPU). We offer both an SDK and applications. The applications support CSV output and recording as standard, so they can be used beyond real-time verification. Two versions are available: a camera-input version and a file-input version.
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basic information
◆ Four Features of the Liveness Detection (Anti-Spoofing) Technology 1. [Fake Face Detection] Detects smartphone-based spoofing Using deep learning, it detects spoofing attacks that present facial images on photographs, printed media, or smartphone screens. 2. [Action Detection] Prevents spoofing by prompting users for random actions Based on time-series data from camera input, it instructs the user to perform specified actions and determines liveness by detecting whether those actions were performed. 3. Camera-agnostic — works even with a smartphone camera This Liveness Detection technology is largely camera-agnostic and can be deployed using a wide range of commercially available cameras, including smartphone cameras and USB webcams. 4. Optimized for Edge AI — high-speed processing on CPU alone Through model optimization and compression, the system delivers high-speed inference even without GPU acceleration. On a modern Intel Core i5/i7 processor, it processes at approximately 100 ms per frame. (Processing speed may vary depending on the device.)
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Applications/Examples of results
Usage: ◆ Liveness detection for facial recognition ◆ Anti-spoofing at photo capture during eKYC Others
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We research, develop, and provide core technologies for human sensing, built on deep-learning-based image processing. ◆ Liveness Detection (Anti-Spoofing) Technology This technology combines "Fake Face Detection," which determines whether a face presented on a photograph or a smartphone screen is a spoof or a real person, and "Action Detection," which determines whether the user performs the instructed action, to prevent spoofing. https://bio-check.pas-ta.io/ ◆ Eye Detection / Blink Detection Technology This highly versatile core technology measures "blinks," "eye feature points," and "viewing distance (the distance from the camera to the eye)" using a webcam or a PC's built-in camera. https://pupil.pas-ta.io/ ◆ Gaze Detection Technology This technology analyzes camera input or video files to detect a person's "gaze direction" and "face orientation." https://eyetrack.pas-ta.io/ ◆ Iris Recognition Technology This recognition technology uses the unique pattern of the "iris" in a person's eye, which is not duplicated between individuals. It is currently provided only to partners engaged in the joint development of iris capture cameras. https://iris.pas-ta.io/
