Face Recognizer Using Python & OpenCV
Jayanta Sarkar, full stack web developer and Python prog
Assista a este curso e milhares de outros
Assista a este curso e milhares de outros
Aulas neste curso
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1.
Introduction
1:05
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2.
Install libraries and read the image file
12:55
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3.
MediaPipe and face detection setup
8:47
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4.
Main loop that going to recognise the face
12:32
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Estudante
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Sobre este curso
Are you ready to take your computer vision skills to the next level? In this hands-on project-based course, you’ll learn how to build a real-time multi-face recognizer using Python, OpenCV, and the face_recognition library.
Whether you're an aspiring AI enthusiast, a Python developer, or a computer vision student, this course will guide you step-by-step in creating a powerful and practical face recognition system capable of detecting and identifying multiple faces in real time using your webcam.
Conheça seu professor
Jayanta Sarkar is a dedicated Python programmer and full-stack web developer with a passion for creating dynamic and interactive web applications. With a robust background in both front-end and back-end development, Jayanta excels in building seamless user experiences and efficient, scalable systems.
Over the years, Jayanta has honed his skills in various programming languages and frameworks, making him proficient in technologies such as JavaScript, CSS, HTML, and MySQL. His expertise extends to developing comprehensive solutions that integrate sophisticated database management with intuitive user interfaces.
Jayanta's journey in the tech industry is marked by a continuous drive to learn and adapt to new technologies. He has developed and published several successf... Visualizar o perfil completo
Projeto prático de curso
"My Family Face Recognizer" — Real-Time Multi-Face Recognition App
Project Description:
For your final project, you'll build a real-time multi-face recognition system that can detect and identify at least 3 different known individuals using a webcam. You will create your own face dataset, encode the faces, and implement logic to recognize them in real time with labeled bounding boxes.
Project Requirements:
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Collect at least three different face images and assign names.
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Encode the faces using face_recognition.face_encodings().
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Use your webcam to capture live video and recognize multiple faces at once.
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Display name labels with bounding boxes for each recognized face.
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Show "Unknown" for unrecognized faces.
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Optional: Save a screenshot automatically when a new face appears.
Tools and Libraries:
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Python
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OpenCV
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face_recognition
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NumPy
Submission Instructions:
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Upload your .py project file
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Submit at least one screenshot showing your system recognizing multiple faces in real time
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Optionally, include a short 20–30 sec screen recording
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