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Ultralytics YOLO11 🚀
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online Attandance from live cctv camera #7337

Closed Margishnextbrain closed 8 months ago

Margishnextbrain commented 9 months ago

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Question

hi @plashchynski @akx @CySlider @nihui project : attendance management system using live cctv camera
I have 1000 users. then i will trained the 1000 images then detect the face recognition from live cctv camera so it possible yolo8 model for trained the 1000 images for face recognition using cctc camera ?

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github-actions[bot] commented 9 months ago

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

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Margishnextbrain commented 9 months ago

@UltralyticsAssistant please help me on this

glenn-jocher commented 9 months ago

@Margishnextbrain Using YOLOv8 for a face recognition task in an attendance management system with a live CCTV camera is feasible, but it's important to understand the strengths and limitations of YOLO models in the context of face recognition.

  1. Strengths of YOLOv8 for Face Detection:

    • Speed and Efficiency: YOLO models are known for their fast processing speed, making them suitable for real-time applications like live CCTV footage analysis.
    • Accuracy: YOLOv8, being an advanced version, likely offers improved accuracy and performance in object detection.

  2. Considerations for Face Recognition:

    • Face Detection vs. Recognition: YOLO models, including YOLOv8, are primarily designed for object detection (identifying the presence and location of faces in this case), not for face recognition (identifying who the face belongs to).
    • Training on Faces: While you can train YOLO models on face datasets, the model would excel in detecting faces but not in distinguishing between different individuals' faces.

  3. Suggested Approach for Your Project:

    • Use YOLOv8 for Face Detection: Employ YOLOv8 to detect faces in the live CCTV footage efficiently.
    • Combine with a Face Recognition System: After detecting faces, use a specialized face recognition system to identify the individuals. Systems like FaceNet, DeepFace, or custom-trained models on frameworks like TensorFlow or PyTorch can be used for this.
    • Dataset Size and Quality: Ensure your dataset is diverse and robust, covering various angles, lighting conditions, and facial expressions for better accuracy.
    • Privacy and Ethics: Implementing such a system involves sensitive privacy considerations. Ensure compliance with relevant data protection and privacy laws.

In summary, YOLOv8 can be an excellent tool for the face detection part of your attendance management system. However, for the face recognition component, integrating a specialized face recognition model would be necessary to identify the individuals from the detected faces.

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arad2022 commented 8 months ago

@UltralyticsAssistant please help me on this

I have the same question. Did you find any solution for that or did you use any other face recognition methods?