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Revolutionizing Face Recognition with AI: A Technical Deep Dive(example.com)

123 points by techwiz 6 months ago flag hide 10 comments

hacker1 6 months ago next
Fascinating article! I've been working on similar AI recognition projects and I can attest to the challenges in creating accurate facial recognition systems. Kudos to the team.
- aiinventor 6 months ago next
  @hacker1 Totally agree, it's a tough problem to solve, but the results are worth it. Do you have any interesting papers to suggest for further reading?
curious_person 6 months ago prev next
What's the difference between this method and traditional machine learning approaches for face recognition?
- smarteye 6 months ago next
  @curious_person Neural networks allow for detecting complex patterns compared to norm-based approaches which struggle to find features in nonlinear conditions. Also, DL algorithms are generally less effected by pose, lighting, and expression changes.
tech_enthusiast 6 months ago prev next
Great write-up! What software libraries and frameworks were used in the development of this AI model?
- deeplarningdev 6 months ago next
  @tech_enthusiast We used TensorFlow for model development and OpenCV for a lot of image and video processing. Keras would have been great for speed, but we needed more control over internal complex layers.
aminority 6 months ago prev next
Any idea how well this system would fair in an LPWA (Low Power Wide Area) IoT sensor network?
- embedded_ai 6 months ago next
  @aminority Due to the computationally-expensive algorithms, I don't think the system would fare well in LPWA settings currently. However, this may change with the advent of more efficient deep learning models.
quant1queries 6 months ago prev next
Data-centric comments like this enrich the HN community. I wonder though, what kind of training data and validation techniques were employed in this research.
- datasetoverlord 6 months ago next
  @quant1queries Good question! A diversified, extensively-labelled dataset was used. It went through rigorous 80-20 train-validate and k-fold cross-validation techniques. Dataset augmentation techniques were also applied to improve robustness.

hacker1 6 months ago next
Fascinating article! I've been working on similar AI recognition projects and I can attest to the challenges in creating accurate facial recognition systems. Kudos to the team.
- aiinventor 6 months ago next
  @hacker1 Totally agree, it's a tough problem to solve, but the results are worth it. Do you have any interesting papers to suggest for further reading?
curious_person 6 months ago prev next
What's the difference between this method and traditional machine learning approaches for face recognition?
- smarteye 6 months ago next
  @curious_person Neural networks allow for detecting complex patterns compared to norm-based approaches which struggle to find features in nonlinear conditions. Also, DL algorithms are generally less effected by pose, lighting, and expression changes.
tech_enthusiast 6 months ago prev next
Great write-up! What software libraries and frameworks were used in the development of this AI model?
- deeplarningdev 6 months ago next
  @tech_enthusiast We used TensorFlow for model development and OpenCV for a lot of image and video processing. Keras would have been great for speed, but we needed more control over internal complex layers.
aminority 6 months ago prev next
Any idea how well this system would fair in an LPWA (Low Power Wide Area) IoT sensor network?
- embedded_ai 6 months ago next
  @aminority Due to the computationally-expensive algorithms, I don't think the system would fare well in LPWA settings currently. However, this may change with the advent of more efficient deep learning models.
quant1queries 6 months ago prev next
Data-centric comments like this enrich the HN community. I wonder though, what kind of training data and validation techniques were employed in this research.
- datasetoverlord 6 months ago next
  @quant1queries Good question! A diversified, extensively-labelled dataset was used. It went through rigorous 80-20 train-validate and k-fold cross-validation techniques. Dataset augmentation techniques were also applied to improve robustness.