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Luxand FaceSDKLuxand FaceSDK

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Luxand FaceSDKSoftware Introduction Download Link

Luxand FaceSDK Screenshot of the interface Luxand FaceSDK (Luxand FaceSDK.exe). Real-time precision face recognition and identification SDK for C++, C#, Objective C, Swift, Java, VB, Delphi, Python.

 
  • Powerful cross-platform FaceSDK for fast, precise facial recognition, biometric ID, and real-time video authentication

    Integrate advanced facial recognition and biometric identification into your apps with FaceSDK. This multi-platform library lets Microsoft Visual C++, C#, Objective-C, Swift, Java, VB, Delphi, and Python developers deliver high-accuracy authentication across Web, Windows, Linux, Mac OS, iOS, and Android. Optimized for 32-bit and 64-bit systems, it supports still images and real-time video streams—ideal for modern AI development and computer-vision workflow automation.

    FaceSDK ensures consistent, precise recognition in nearly any lighting environment and works seamlessly with standard webcams. Using motion-based video identification, it automatically detects, tracks, and tags faces in real time with no prior enrollment. Built for multi-core performance, it identifies up to 70 facial features with exceptional speed, making it a dependable solution for security apps, smart camera systems, and high-volume image processing pipelines.

    Loaded with features and multilingual code samples, FaceSDK helps teams build biometric authentication, hands-free login, and face-driven automation in minutes. It’s trusted by entertainment, security, and imaging industries for reliable face analysis, powering everything from photo editors to enterprise access-control solutions—perfect for developers seeking scalable AI image recognition or high-accuracy identity verification tools.

    • Fast, precise face recognition and biometric identification with real-time support
    • Cross-platform development for Web, Windows, Linux, Mac OS, iOS, and Android
    • Motion-based video identification with automatic face tagging
    • Multi-core optimization for detecting 70+ facial features instantly
    • Includes extensive samples for rapid implementation in multiple programming languages

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