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Português
$
USD
I will build face recognition, object detect, attendance system in deep learning,opencv
Bimh
Sobre este Serviço
Automate your access tracking with secure biometric validation. I will build a premium face recognition and attendance logging system utilizing deep learning and OpenCV. Say goodbye to manual check-ins and proxy attendance with smart, automated biometric identity management.
Using state of the art embedding models like FaceNet, InsightFace, or Dlib, I develop systems that map facial structures into unique mathematical signatures, enabling instant matching against a database of registered profiles. The system operates quickly, verifying identities in fractions of a second.
What I deliver: a complete user face registration pipeline, high-speed multi-face detection in live camera streams, automated database logging (MySQL, SQLite, or Excel/CSV), and foundational liveness detection to block print/photo spoofing attempts. Streamline your workflow operations with a professional solution. Contact me today to secure your custom attendance build!
Conheça mais sobre Bimh
Bimh
AI systems, YOLO, OpenCV, Deep Learning, Machine Learning, Object Detection
- A partir deDinamarca
- Membro desdemai. de 2026
Idiomas
Inglês
I build advanced AI systems using YOLO, OpenCV, Deep Learning, and Machine Learning technologies. My services include object detection, facial recognition, image segmentation, medical imaging, pose estimation, lane detection, skeleton tracking, and real-time AI applications. I specialize in custom dataset training, image annotation, AI deployment, and Python-based computer vision solutions. I help businesses, startups, and researchers create scalable AI-powered software with accurate detection and optimized model performance.
Perguntas frequentes
Which models do you use for high-accuracy face matching?
I use highly reliable models such as InsightFace, FaceNet, and deep Face architectures to maintain accuracy across varied lighting conditions.
How does the system prevent people from holding up a smartphone photo to cheat?
I integrate custom face liveness detection mechanisms that analyze texture or blink behaviors to reject digital screens and paper printouts.
Can the system recognize faces with masks or glasses on?
Modern embedding models like InsightFace handle standard accessories well, though extreme obstructions may reduce overall precision.
