This tutorial explains how to implement face, eye, and mouth detection using OpenCV's Haar cascades, Dlib's HOG and CNN detectors, and provides step‑by‑step code for both static images and real‑time video streams in Python.
This article explains the fundamentals of face detection using the Haar‑cascade algorithm, how to train and apply detectors with OpenCV, and introduces the Local Binary Patterns Histograms (LBPH) method for face recognition, covering data preparation, parameter selection, and matching techniques.
This guide explains how to prepare Haar and LBP data, use OpenCV's CascadeClassifier and detectMultiScale functions, and run a complete Python script that captures video, detects faces, draws bounding boxes, displays results, and saves detected frames.
