IDLive Face SDK¶

IDLive Face SDK bundles IDLive Face as a set of libraries you can use to develop your application with C++, Python, Java or C#. IDLive Face can run on CPUs or Nvidia GPUs, and the SDK is available for Linux and Windows. There is also an SDK for mobile devices running iOS and Android.

Android release is available in 2 forms:

Single AAR package containing SDK libraries, compiled Java classes and data files, with support of Armv7-A, Armv8-A and x86. This release has Java API.
Since 1.43.0: set of SDK native libraries built for Armv8-A. This release has C API.

Software requirements¶

We provide desktop releases for:

Linux with glibc 2.27+ (Ubuntu 18.04, CentOS 8).
Windows 10/11 or Windows Server 2016+.

GPU version requires Nvidia CUDA 11.2 and cuDNN 8.

To use C++ and C API you need:

GCC 7.5+ for Linux.
Microsoft Visual Studio 2019+ for Windows.

If you plan to use language wrappers we support:

Python 3.8–3.10
Java 8+
C# for Microsoft .NET Framework 4.0+ or .NET Core 3.0+ (Windows only)

Mobile releases support:

Android 21+
iOS 12+

Hardware requirements¶

For the CPU version you need the CPU that supports at least AVX2 instruction set. More cores and higher clock rates will yield better processing times. We recommend using as many cores as you possibly can, but note that NUMA configurations are not supported. Make sure that you use the single-socket system. The memory usage depends on the number of CPU cores and the size of a workload. We recommend to start from 4 Gb and increase it if needed.

For the GPU version you need the Nvidia GPU with the compute capability from 6.1 to 8.6. You also need a reasonable CPU, to make sure that workload for the GPU is generated fast enough. We recommend to have 2 cores if you plan to process a single image only, and up to 8 if you plan to process several images at once. As with the CPU version the memory requirements depend on your workload, but for the GPU version you should start from 8 Gb. Your GPU also needs to have at least 8 Gb of memory.