Mohamad Rizal Syafi'i
Computer Vision
Image Analysis
This project provides various image processing techniques using OpenCV and Python, offering insights into image characteristics, transformations, and facial recognition, such as Channel Analysis, Histogram Calculation, Filtering Techniques, Contrast & Brightness Enhancement, and Face Detection.
Vision Annotation
This project utilizes OpenCV to detect eyes and annotate them in real time while also detecting a chessboard and numbering its corners sequentially. It performs two key tasks, such as Eye Detection & Annotation and Chessboard Corner Numbering. This project is useful for both facial recognition applications and computer vision calibration.
Depth Mapping
This project leverages computer vision to perform depth analysis and index mapping, enabling enhanced depth perception and visualization in images, such as Depth Map Generation and Index Mapping for Depth Analysis. This project is useful for applications in 3D reconstruction, autonomous navigation, augmented reality, and medical imaging.
Online Salon
This project leverages OpenCV and computer vision techniques to apply virtual hair filters in real-time, providing a fun and interactive experience. Detects faces using the Haar Cascade Classifier and overlays a selection of virtual hairstyles onto the user's image. Users can experiment with different hairstyles in real time, offering a novel approach to virtual hair styling.
Whos Detected
This project utilizes Convolutional Neural Networks (CNNs) to identify and classify individuals whose faces are detected in real-time video streams. Employs deep learning techniques for face detection and classification. Using CNNs, the model can recognize and differentiate between multiple individuals based on a labelled dataset.
Count Face Video
This project brings the magic of Facial Recognition into action, identifying and tracking faces in videos with the power of PCA & KNN. The AI-powered system detects, identifies, and counts faces in videos. Leveraging Principal Component Analysis (PCA) and K-Nearest Neighbors (KNN), this project processes video files to track face occurrences over time.
