1 Overview
Faces can be generally categorized into three types: real faces, faces in images, and faces in videos. Living refers to the real living human faces, while non-living refers to the faces in images or videos.
Liveness detection can distinguish real faces from photos, videos, and other spoofing and automatically determine if the face on the screen is the face of a real person. By accurately detect printed photos and images and videos displayed on the screen, spoofing attacks can be effectively prevented.
1.1 Technical principles
Silent liveness detection technology is divided into three parts: face detection, liveness detection, and face image acquisition.
Face detection, positioning and capture: the system uses algorithm to find the position of the face from the image taken by the camera to determine the position, and the face size is calculated according to its characteristic value, and then process it.
Livneess detection: learn the characteristics and differences of living and non-living bodies through deep learning methods, determine whether the face image is captured from a living body, and effectively prevent attacks by photos, video remakes, and mask models.
Face image acquisition: collect high-quality face images during the detection process.
1.2 Function introduction
Silent liveness detection provides an anti-spoofing detection solution by collect face images through mobile phone cameras and determine whether the face images come from the correct person.
After completing the liveness detection, the returned face image and encrypted binary stream file can be used as input parameters to call the API server (private cloud) interface for face comparison.
1.3 Current version and compatibility
The latest version of the Silent Liveness SDK (Android version) is V2.4.4 (Updated on August , 2021).
The size of the installation package is MB.
It supports Android 4.0 or later systems, and devices with arm v7 or arm v8 NEON instruction set.
1.4 Sample
Import Android Studio Sample project and run it successfully. Click [Start Detection] button to start the liveness detection.
During the liveness detection process, prompts are given according to face status.
After successful detection, a corresponding face image will be returned.
1.5 License introduction
Open the license file (xxx.lic) to view the corresponding license expiration period Expiration and license identifier License SN.
If package name error is reported after importing a license into a project, check whether the package name entered in the project is included in the list of bundled package names in the license. If license file is invalid is reported, check whether the imported file is correct and if the problem persists, please contact the support stuff and provide the License SN in the license file.
1.5.1 How to use
The provided SDK sample does not contain the license file by default. After obtaining the license file, the file must be renamed and placed into the specified path before running. The authorization method of SDK sample for Android silent liveness detection is described in detail as follows:
1.5.2 Trial license instructions
Rename the license file (xxx.lic) to SenseID_Liveness_Silent.lic
Place the SenseID_Liveness_Silent.lic file under the directory of common-silent/src/main/assets
Ensure that the license file name matches with
LICENSE_FILE_NAMEin the project code
1.5.3 Official license instructions (requires package name)
Rename the license file (xxx.lic) to SenseID_Liveness_Silent.lic
Place the SenseID_Liveness_Silent.lic file under the directory of common-silent/src/main/assets
Ensure that the name of the license file is the same as the one in the project code
LICENSE_FILE_NAMEChange the package name corresponding to applicationId in sapmle ---> build.gradle to the package name that is actually bound to the license.
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