Notes

Title - Automated Physiognomy Appearance System
Technologies used - Python, Machine Learning, Arduino, FTDI and some Hardware components.
Project Summary-
Data Collection: A dataset of face images is collected. This can be done using a camera or by downloading images from online sources.
Face Detection: The OpenCV library provides built-in functions for detecting faces in an image or video stream. The Haar Cascade classifier or deep learning-based models such as Single Shot Detector (SSD), You Only Look Once (YOLO), and Faster R-CNN can be used for face detection.
Face Alignment: The detected face may be tilted or rotated, so it is necessary to align it before feature extraction. OpenCV provides several methods for face alignment, including affine transformation, perspective transformation, and face landmark detection using the Dlib library.
Feature Extraction: In this step, a set of discriminative features is extracted from each face image. OpenCV offers several feature extraction methods, including Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and Scale-Invariant Feature Transform (SIFT).
Face Recognition: After feature extraction, the system compares the features of the input face image to the features of faces in the dataset to determine a match. This can be done using various machine learning techniques such as k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), or Deep Neural Networks (DNN).
Face Tracking: In video surveillance applications, face tracking is crucial. OpenCV provides several object tracking algorithms such as MeanShift, CamShift, and Optical Flow for face tracking.
Overall, a face recognition system using OpenCV can be built by combining these steps in a pipeline. However, the accuracy of the system depends on the quality of the data collected, the selection of appropriate face detection and recognition algorithms, and the optimization of the parameters.