366 skills found · Page 6 of 13
alisharify7 / Face DetectionPython Real Time Face Detection application, using opencv, deepface
BernardNyarko / Smart Monitor An AI Powered IoT Monitoring System For Small Medium Scale PremisesWith recent advances in both Artificial Intelligence (AI) and Internet of Things (IoT) capabilities, it is more possible than ever to implement surveillance systems that can automatically identify people who might represent a potential security threat to the public in real-time. Imagine a surveillance camera system that can detect various on-body weapons, suspicious objects, and traffic. This system could transform surveillance cameras from passive sentries into active observers, which would help prevent a possible mass shooting in a school, stadium, or mall. In this project, we tried to realize such systems by implementing Smart-Monitor, an AI-powered threat detector for intelligent surveillance cameras. The developed system can be deployed locally on the surveillance cameras at the network edge. Deploying AI-enabled surveillance applications at the edge enables the initial analysis of the captured images on-site, reducing the communication overheads and enabling swift security actions. We developed a mobile app that users can detect suspicious objects in an image and video captured by several cameras at the network edge. Also, the model can generate a high-quality segmentation mask for each object instance in the photo, along with the confidence percentage. The camera side used a Raspberry Pi 4 device, Neural Compute Stick 2 (NCS 2), Logitech C920 webcam, motion sensors, buzzers, pushbuttons, LED lights, Python Face recognition, and TensorFlow Custom Object Detection. When the system detects a motion in the surrounding environment, the motion sensors send a signal to the Raspberry Pi device notifying it to start capturing images for such physical activity. Using Python’s face recognition and TensorFlow 2 custom object detection Smart-Monitor can recognize eight classes, including a baseball bat, bird, cat, dog, gun, hammer, knife, and human faces. Finally, we evaluated our system using various performance metrics such as classification time and accuracy, scalability, etc.
santhalakshminarayana / Face RecognitionFace recognition with VGG face net in Tensorflow and Keras python.Trained in Colab.
Kalyan-Koppula / Face Recognition Based Attendance SystemPython implementation of simple face recognition based attendance system using face_recognition library.
svetlana-topalova / EigenfacesA Jupyter Notebook that implements in Python 3 the Eigenfaces algorithm for face recognition
ibarakaiev / Face RecognitionFace recognition and segmentation using Python, dlib, and One Millisecond Face Alignment with an Ensemble of Regression Trees.
thomasnynas12 / OpenCV Face RecognitionReal-time face recognition project with OpenCV and Python
nancypareta / Stress Detection Techniques And Chat Bot Depression TherapyThe Real time emotion recognition model will return the emotion predicted in real time. The model classifies face as stressed and not stressed. A model is trained on the fer2013 dataset (https://www.kaggle.com/deadskull7/fer2013) .The stress level is calculated with the help of eyebrows contraction and displacement from the mean position. The distance between the left and right eyebrow is being calculated and then the stress level is calculated using exponential function and normalized between 1 to 100. Chatbot-Depression Therapy to provide real time therapeutic solutions to alleviate depression. Chatbot System is implemented using deep learning for detection and management of stress and depression and provide suggestions accordingly based on user’s mental condition. Technologies: Keras, genism python libraries, anaconda environment, the dataset being used is obtained from Kaggle.
aquibjaved / Real Time Face Recognition In Python Using Opencv Real time face recognition in python using opencv, Opencv 2.4.10 python 2.7.10
Santhu195 / Face LoginFace login using face recognition by Open CV Python
swatijha2496 / FACE RECOGNITION USING OPENCV IN PYTHONFace is most commonly used biometric to recognize people. Face recognition has received substantial attention from researchers due to human activities found in various applications of security like airport, criminal detection, face tracking, forensic etc. Compared to other biometric traits like palm print, Iris, finger print etc., face biometrics can be non-intrusive. They can be taken even without user’s knowledge and further can be used for security based applications like criminal detection, face tracking, airport security, and forensic surveillance systems. Face recognition involves capturing face image from a video or from a surveillance camera. They are compared with the stored database. Face biometrics involves training known images, classify them with known classes and then they are stored in the database. When a test image is given to the system it is classified and compared with stored database. Face biometrics is a challenging field of research with various limitations imposed for a machine face recognition like variations in head pose, change in illumination, facial expression, aging, occlusion due to accessories etc.,. Various approaches were suggested by researchers in overcoming the limitations stated. 72 Automatic face recognition involves face detection, feature extraction and face recognition. Face recognition algorithms are broadly classified into two classes as image template based and geometric feature based. The template based methods compute correlation between face and one or more model templates to find the face identity. Principal component analysis, linear discriminate analysis, kernel methods etc. are used to construct face templates. The geometric feature based methods are used to analyze explicit local features and their geometric relations (elastic bung graph method). Multi resolution tools such as contour lets, ridge lets were found to be useful for analyzing information content of images and found its application in image processing, pattern recognition, and computer vision. Curvelets transform is used for texture classification and image de-noising. Application of Curvelets transform for feature extraction in image processing is still under research.
9andrea1 / Opencvopencv python script for face detection and recognition, people detection and more
lechugalf / Face IdFace recognition from a picture or web cam video with face recognition module in python
Amal4m41 / QRcode FaceRecognition Door Lock System Arduino IOTDoor lock system using Arduino and Python with 2 step authentication(Face Recognition and QR code)
ananta15 / SmartAttendanceAttendance system using face recognition. Built in python using Tkinter as UI and MySQL as database
18D070001 / Facial Recognition Based Attendence SysytemFace recognition based Attendance Management System by using OpenCV and python with a Tkinter GUI interface.
sumanthd17 / Face RecognitionFace Recognition based attendance system for classroom environment. Developed a python API which recognizes the people in a picture(of a classroom) and matches them with all the student registrations for that course and returns a image with all the recognized and unrecognized faces (face tags), and tags all the students recognized as present.
serhanelmacioglu / Face Recognition Coding With PythonThis Face Recognition project detects faces and places a frame around them and identifies the face based on those in a given list. It works by analyzing a photo and comparing it to the faces in the list to determine if it is a match or if it is an unknown identity.
kby-ai / FaceRecognition WindowsFace Recognition Windows, Face Liveness Detection, Face Search, Face Identification, Face Matching, Face Comparison, Facial Recognition
FreeBirdsCrew / Real Time Face RecognitionReal Time Face Recognition with Python and OpenCV2, Create Your Own Dataset and Recognize that. #FreeBirdsCrew
