This is my first Internship Project on Deep Learning. This is a challenge of WIDER Face Benchmark whose aim is to detect faces in the images in any condition of various poses, illuminations and occlusions. And we managed to get the accuracy of 91% in detecting every type of images.
Built on OpenCV 3.2.0 and Python 3.6.0/Anaconda 4.3.0. Code to detect faces using Haar Cascade and match faces using LBPH (Local Binary Patterns Histogram) Recognition on a live web camera.
This project is an app that lets the user control the mouse pointer of their computer using their eye gaze. The app uses multiple pre-trained computer vision models from the openvino model zoo in a structured pipeline to detect the user's eye gaze and move the mouse pointer in the right distance and direction.
DIY surveillance system with a face detection implemented with Python, OpenCV and Flask. Made as a team project for a System Security course at the Faculty of Organization and Informatics, Varaždin, Croatia.
😨 Detects faces by using OpenCV which is computer vision interface library or platform like Matlab. OpenCV provides classifiers for detecting a different type of objects by using a different kind of sensors and cameras.
Using MTCNN and YOLOv5 to detect and blur ads and faces in real-time. Overview of real-time object detection and instance segmentation methods. Tutorial on YOLOv5 and MTCNN.
Deep Learning Nanodegree Project : Given an image of a dog, the algorithm will identify an estimate of the canine’s breed. If supplied with an image of a human, the code will identify the resembling dog breed.