Built on technology jointly patented with Panasonic, our Liveness Detection technology is a biometric security core technology designed to prevent spoofing and hacking in systems such as facial recognition and eKYC (online identity verification).
▼ Demo apps and the trial SDK are available in Japan only.
For the Liveness Detection API, please contact us.
A deep learning-based liveness detection technology that works with ordinary visible-light cameras, such as webcams (USB cameras) and built-in PC cameras.
It prevents spoofing through Fake Face Detection (Passive Liveness Detection), which can evaluate a single frame, and Action Detection (Active Liveness Detection), which uses time-series data to detect specified actions such as blinking and head movements. Through model optimization and compression, it achieves a processing speed of approximately 100 ms per frame even on a CPU (measured on an 8th-generation Intel Core i7 Windows machine). It also supports CSV export and video recording for verification beyond real time. Camera-input and file-input editions are available.
The Windows demo app is available in Japan only. See the documents section for materials.
A demo app that lets you experience liveness detection designed to prevent impersonation using photos or screens during facial recognition, for use cases such as eKYC (online identity verification).
It detects actions such as head orientation, gaze direction, blinking, and putting on or removing glasses, effectively preventing spoofing attempts that use another person's face displayed on a smartphone screen or photo.
* The iOS edition is currently distributed via TestFlight only (not via the App Store).
* The Android edition is distributed via a download URL link for Google Play.
A liveness detection library that judges spoofing based on time-series changes in facial states (head orientation, gaze direction, blinking, etc.) and determines from a single frame whether a face is genuine or a fake shown on a smartphone or photo.
The SDK can be integrated into general-purpose operating systems such as Windows, Linux, iOS, and Android. The trial SDK is available in Japan only; the structure of the SDK is shown below.
| Supported OS | Main Contents | Notes |
|---|---|---|
| Windows | Header files (.hpp) Library files (.dll / .lib) Learning model files Sample app Activation token |
The sample app can be chosen from a .NET app or a console app. |
| Linux | Header files (.hpp) Library files (.so) Learning model files Sample app Activation token |
The sample app is a console app. It also runs on Raspberry Pi and Jetson. |
| iOS | Framework-format files Learning model files Sample app Activation token |
A sample wrapper file that calls the C++ library is included. |
| Android | Header files (.hpp) Library files (.so) Learning model files Sample app Activation token |
A sample wrapper file that calls the C++ library from Java via the NDK is included. CPU architectures arm64-v8a / armeabi-v7a / x86 / x86_64 are available. |
| Other OS | Please contact us via the inquiry form. | |
The SDK Interface Specification is available in the documents section.
~ Liveness Detection protects the security of
facial recognition and eKYC ~
Using deep learning, the system detects spoofing attacks that attempt to impersonate a user by presenting facial images on photographs, printed media, or smartphone screens.
■ [YouTube] Spoofing Attack Detection Demo
A demo video illustrating the detection of "spoofing attacks" that use someone else's facial image during face recognition at devices such as access-control terminals.
With our Liveness Detection model, when a genuine face is presented to the camera the result "Real" is shown in a blue frame, and when a fake face is presented the result "Fake" is shown in a red frame.
See how attacks via smartphone, tablet, printed media, and photographs are detected with high accuracy.
<Video> Spoofing attack detection via smartphone, tablet, printed media, and photographs
Based on time-series data from camera input, the system instructs the user to perform actions — (1) head orientation, (2) gaze direction, (3) blinking, (4) putting on/removing glasses, (5) putting on/removing a mask — and evaluates liveness based on whether the specified actions are detected using deep learning.
By requesting multiple actions in a random sequence, it helps prevent replay attacks using pre-recorded videos.
Actions used in the Liveness Detection technology
Our 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.
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; even on a modern multi-core iPhone CPU, it achieves under 150 ms per frame. (Processing speed may vary depending on the device.)
We provide our Liveness Detection technology as a high-performance API, so you can easily integrate this capability into your web services and applications — dramatically reducing development time compared with building it from scratch. (A self-contained SDK is also available in Japan only.)
In addition to iOS and Android apps for testing accuracy, a Windows-based GUI verification app is available.
With video recording and CSV export, it can be used for pre-purchase validation and research purposes. (Available in Japan only.)
Liveness Detection can be seamlessly added to identity verification workflows such as eKYC (online identity verification).
Patents used in the Liveness Detection technology
| Patent Registration No. | Patentee | Title | |
|---|---|---|---|
| 1 | JP6956986 US12223774B2 CN115039150B IN590332B1 |
Swallow Incubate Co., Ltd. Panasonic Corporation |
Determination method, determination device, and determination program (Granted in Japan, the United States, China, and India) |
Trial apps (smartphone / Windows) and the trial SDK are currently available in Japan only.
For the Liveness Detection API, please contact us to request access.
Pricing for the app and SDK is available in Japan only.
For Liveness Detection API pricing, please contact us. Estimates are free of charge.
Download free technical documentation and integration guides for this technology.
64 pages
The interface specification for the Liveness Detection technology.
67 pages
User guide for the Windows app (camera-input edition).
67 pages
User guide for the Windows app (file-input edition).
29 pages
A manual for integrating the Liveness Detection SDK into a .NET app.
27 pages
A manual for integrating the Liveness Detection SDK into an iOS app.
43 pages
A manual for integrating the Liveness Detection SDK into an Android app.
