22 skills found
mrnugget / Opencv Haar Classifier TrainingLearn how to train your own OpenCV Haar classifier
austinjoyal / Haar Cascade FilesA complete collection of Haar-Cascade files. Every Haar-Cascades here!
anupsarkar-dev / ExoVisixAuto Attendance System Using Real Time Face Recognition With Various Computer Vision & Machine Learning Tools
noahlevenson / WasmfaceViola-Jones face detection from scratch in WebAssembly
amannirala13 / HAAR Cascade Trainer Linux🖼️ This is a HAAR Cascade Classifier training GUI application for Linux. This application make it really easy to train classifiers for object detection and tracking using opencv by providing a Graphical user interface to set parameters and perform necessary steps.
vbajpai / HaartrainingCar Detection using Haar Training
DarkmatterVale / Opencv Haar TrainingOpenCV 3 haar cascade training [DEPRECATED - No longer Actively Developed]
neelgajjar / Neural Network Gender Classification Skin SegmentationNeural Network-male-female-recognition-human-skin-detection
raunakdoesdev / Haar TrainingA comprehensive library for training Haar Cascades on Windows machines (primary operating system of my robotics team). All necessary files are compiled and stored within the repository with detailed usage instructions.
anastasiabolotnikova / AdaboostAdaboost Haar feature classifier cascade training
Paradiddle131 / Cigarette Detection With Haar Cascade ClassifierCustom Haar Cascade training for detection of cigarettes in the images and videos
gale31 / ObjectDetectorObjectDetector uses OpenCV Haar cascade classifiers to detect different objects in images and videos with Python. For now, this repository includes my trained haar cascade classifier for detecting cars, the default haar cascade classifier for human faces (haarcascade_frontalface_default), a classifier for bananas from CodingRobin and a classifier for wallclocks which are used and tested in programs, detecting the objects from image/video, comparison between different human body parts classifiers and some other programs, which (will) help training the classifiers (for example, a program downloading the "cat box" synset images from ImageNet).
shivangbansal / Haar Cascade Ear TrainingNo description available
eechhx / Opencv Cascade TrackerTraining, detecting, and tracking a Haar Cascade in OpenCV with Python
bmakowe / Opencv Haar TrainingNo description available
dhruvvyas90 / Haar Object MarkerA small python tool for creating positive text file for OpenCV haar training.
danilolekovic / Haar NegativePython script for getting negative samples quickly and efficiently for HAAR cascade training.
zanazakaryaie / Cascade ToolboxA toolbox to simplify training, testing, and running HAAR/LBP cascades for object detection
naveenaks / Video Emotion RecognitionFacial expressions play an important role in identifying the emotional state of an individual and help others relate, understand and respond accordingly. Individuals can have different reactions to the same stimuli. This project aims to examine the emotional state of patients experiencing psychosis.The objective is to detect the various emotions of these patients, these emotions might include anger, disgust, fear, joy, sadness, surprise or neutral. This is achieved by making the patient play a game while their facial expressions are obtained through a live web camera. This is used to monitor and record their emotions as data for medical purpose. Implementing the Haar Algorithm, the frames are cropped and the face alone is procured on which grey scaling and resizing process is carried out. Now the sequence of faces obtained will be used to extract the most necessary features by a CNN - 2D(Convolutional neural network) to extract the most necessary features of each face, which will encode motion and facial expressions to predict emotion. Two sets of data are used as the dataset - Training set- the algorithm will read, or ‘train’, on this over and over again to try and learn its task, and the Testing set - the algorithm is tested on this data to see how well it works.
arafat4 / An Autonomous Attendance System Based On Machine Learning ConceptThe attendance check is hugely crucial in the management of the classroom. The time-consuming way of taking attendance is by the following prior methods such as signing the name in a paper sheet or calling the names of each student. In particular, it is vulnerable to cheating and other fraudulent activity. Artificial Intelligence based applications benefit from face recognition, which is a valuable piece of work. The suggested system aims to develop a comprehensive face recognition system capable of dealing with many photos. The central technological concept of this system is Machine Learning to detect human faces and identify a person. Haar-Cascade is a face detection algorithm that identifies the face from a sequence of photos used for training and testing. The dataset uses negative and positive images to train the algorithm called Local Binary Pattern Histogram (LBPH) and the same data has been used to check the system. The LBPH algorithm is responsible for recognizing faces in input images. Python Tkinter is very effective in making desktop GUI software, So the core element of the developed system is Tkinter. The database of the system is MySQL. With the capability of this technology trialed in a class, the outcome result is highly positive — a survey completed among students to inspect the students’ opinion and find out the advantages and disadvantages.
