a simple face detector for batch processing

This is a simple Raspberry Pi OpenCV face detector

Simple and Effective Face Detector, based on Progressive Calibration Networks (PCN) which is an accurate rotation-invariant face detector running at real-time speed on CPU, published in CVPR 2018.

Image segmentation is the process of dividing an image into multiple parts. It is typically used to identify objects or other relevant information in digital images. There are many ways to perform image segmentation including Thresholding methods, Color-based segmentation, Transform methods among many others. Alternately edge detection can be used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. Image thresholding is a simple, yet effective, way of partitioning an image into a foreground and background. This image analysis technique is a type of image segmentation that isolates objects by converting grayscale images into binary images. Image thresholding is most effective in images with high levels of contrast. Otsu's method, named after Nobuyuki Otsu, is one such implementation of Image Thresholding which involves iterating through all the possible threshold values and calculating a measure of spread for the pixel levels each side of the threshold, i.e. the pixels that either fall in foreground or background. The aim is to find the threshold value where the sum of foreground and background spreads is at its minimum. Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. An image can have horizontal, vertical or diagonal edges. The Sobel operator is used to detect two kinds of edges in an image by making use of a derivative mask, one for the horizontal edges and one for the vertical edges. 1. Introduction Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. Face detection can be regarded as a specific case of object-class detection. In object-class detection, the task is to find the locations and sizes of all objects in an image that belong to a given class. Examples include upper torsos, pedestrians, and cars. Face-detection algorithms focus on the detection of frontal human faces. It is analogous to image detection in which the image of a person is matched bit by bit. Image matches with the image stores in database. Any facial feature changes in the database will invalidate the matching process. 2. Needs/Problems There have been widely applied many researches related to face recognition system. The system is commonly used for video surveillance, human and computer interaction, robot navigation, and etc. Along with the utilization of the system, it leads to the need for a faster system response, such as robot navigation or application for public safety. A number of classification algorithms have been applied to face recognition system, but it still has a problem in terms of computing time. In this system, computing time of the classification or feature extraction is an important thing for further concern. To improve the algorithmic efficiency of face detection, we combine the eigenface method using Haar-like features to detect both of eyes and face, and Robert cross edge detector to locate the human face position. Robert Cross uses the integral image representation and simple rectangular features to eliminate the need of expensive calculation of multi-scale image pyramid. 3. Objectives Some techniques used in this application are 1. Eigen-face technique 2. KLT Algorithm 3. Parallel for loop in openmp 4. OpenCV for face detection. 5. Further uses of the techniques

SORT (A simple online and realtime tracking algorithm for 2D multiple object tracking in video sequences) and SSD face detector.

A simple face detection tool for COMP6223

This is a simple image classification project trained on the top of Keras/Tensorflow API with MobileNetV2 deep neural network architecture having weights considered as pre-trained 'imagenet' weights. The trained model (mask-detector-model.h5) takes the real-time video from webcam as an input and predicts if the face landmarks in Region of Interest (ROI) is 'Mask' or 'No Mask' with real-time on screen accuracy.

A simple face detector for the Simpsons Characters. Used on seasons 25-28 it created a database of 9,878 images.

A simple face and smile detector for Raspberry Pi and Sense HAT.

Face detection example in QML with OpenCV

AI Slop Detector is a privacy-first browser extension that locally detects AI-generated images (and video frames) on webpages using an ensemble of two Hugging Face models—no uploads, no cloud, full control. Requires running a simple Python backend on localhost for high-accuracy, offline inference.

Simple Object and Face detector using Google APIs and TensorFlow Lite.

Human face detection by computer systems has become a major field of interest. Face detection algorithms are used in a wide range of applications, such as security control, video retrieving, biometric signal processing, human computer interface, face recognitions and image database management. However, it is difficult to develop a complete robust face detector due to various light conditions, face sizes, face orientations, background and skin colors. In this project, we propose a face detection method for color images. Face detection is concerned with finding whether or not there are any faces in a given image and, if present, returns the image location and content of each face. Security and surveillance are the two important aspects of human being. In this project we implemented face detection and recognition system that will capable of processing images very fast while acquiring very high true positive face detection rate. Most face detection algorithms are designed in the software domain Band have a high detection rate, but they often require several seconds to detect faces in a single image, a processing speed that is insufficient for real-time applications. This project is a simple and easy hardware implementation of face detection system using Raspberry Pi, which itself is a minicomputer of a credit card size and is of a very low price. The system is programmed using Python programming language.

A simple Face Detector Mobile App

Simple video decoder and face detector using OpenCV

A simple golang wrapper for DLIB exposing Face Detector and Face Feature predictor

A very basic face detection app using swift

A simple face-mask detector

a simple face detector for batch processing for linux/unix

A simple face detector, which will give gender and age information, based on face++.

Face Detector, is just a simple project to detect faces in given images written in Java using OpenCV library.

Simple concentration scoring algorithm && Blazeface face detector Tensorflow2.0 implementation. (real-time, on web)

Python implementation of my bachelor's dissertation. A simpler face detector that uses a kalman filter and template match to improve performance

This is a simple project and an extension of the face detector where in we are detecting the smile of the person

Simple face detector

Detect faces with Google Machine Learning Kit.

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Simple Face Detector in Python

A simple face-touch detector

Simple Face Mask detector in TensorflowJS