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Comprehensive Guide to Face Recognition Using OpenCV and DeepSORT: Resources for Computer Science Students in Malaysia
Face recognition is a complex problem that requires robust and accurate methods to achieve reliable results. The strategy chosen will rely on the particular application and the resources available.
In this post, we provide a comprehensive list of all the lessons and resources needed by a computer science student in Malaysia to master face recognition using OpenCV and DeepSORT. Whether you are working on academic projects or preparing for a career in computer vision, these resources will guide you through each step of the process.
The post lists all the lessons & resources needed by a computer scicnce student in Malaysia: This inculde using openCV and DeepSORT python code
List of Classes and Associated Resources
Class 1 (27 OCt)
a) Video :
** Part 1 (Facial Detections with OpenCV Class 1 (part 1 - Steps to use python 3.7 with Yolo8 in a virtual venv)
** Part 2 (Facial Detections with OpenCV Class 1 (part 2 - Suggestions for fixing the Deep Sort code Part) )
b) Files:
a) G-Drive (all files) ,
b) Virutal environment (Alia_venv)
c) Slides (pdf file with snap shots from the commands and the code )
d) Sample Videos for Facial & object detection: Github Repo
c) Project Code:
a) Webcam code (DeepSORT tracking implmented ),
b) Using Image code (this code uses elon.jpg file to test faces detection),
c) Using MP4 Video code (DeepSORT tracking implmented and it need test4.mp4),
d) pkl files (face encoding files saved in the binary format with extension.pkl),
e) Other files
d) Excercises : N/A
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