OpenCV is a video and image processing library and it is used for image and video analysis, like facial detection, license plate reading, photo editing, advanced robotic vision, and many more.

OpenCV – Face Detection in a Picture

1. Step 1: Load the OpenCV native library. While writing Java code using OpenCV library, the first step you need to do is to load the native library of OpenCV using the loadLibrary().
2. Step 2: Instantiate the CascadeClassifier class.
3. Step 3: Detect the faces.

How OpenCV’s face recognition works. To apply face detection, which detects the presence and location of a face in an image, but does not identify it. To extract the 128-d feature vectors (called “embeddings”) that quantify each face in an image.

For general computer vision problems, OpenCV’s Caffe model of the DNN module is the best. It works well with occlusion, quick head movements, and can identify side faces as well. Moreover, it also gave the quickest fps among all.

So what is Haar Cascade? It is an Object Detection Algorithm used to identify faces in an image or a real time video. The algorithm uses edge or line detection features proposed by Viola and Jones in their research paper “Rapid Object Detection using a Boosted Cascade of Simple Features” published in 2001.

In order for the system to function, it’s necessary to implement three steps. First, it must detect a face. Then, it must recognize that face nearly instantaneously. Finally, it must take whatever further action is required, such as allowing access for an approved user.

Face detection algorithms typically start by searching for human eyes — one of the easiest features to detect. The algorithm might then attempt to detect eyebrows, the mouth, nose, nostrils and the iris.

Haar Classifier
Fortunately, OpenCV already has two pre-trained face detection classifiers, which can readily be used in a program. The two classifiers are: Haar Classifier and. Local Binary Pattern(LBP) classifier.

Eigenface based algorithm used for Face Recognition, and it is a method for efficiently representing faces using Principal Component Analysis.

on CNN (Convolutional Neural Network) has become the main method adopted in the field of face recognition. To simplify the CNN model, the convolution and sampling layers are combined into a single layer. Based on the already trained network, greatly improve the image recognition rate.

OpenCV is a Library which is used to carry out image processing using programming languages like python. This project utilizes OpenCV Library to make a Real-Time Face Detection using your webcam as a primary camera. Approach/Algorithms used: This project uses LBPH (Local Binary Patterns Histograms) Algorithm to detect faces.

Face detection is a computer vision technology that helps to locate/visualize human faces in digital images. This technique is a specific use case of object detection technology that deals with detecting instances of semantic objects of a certain class (such as humans, buildings or cars) in digital images and videos.

A classifier is essentially an algorithm that decides whether a given image is positive(face) or negative(not a face). A classifier needs to be trained on thousands of images with and without faces. Fortunately, OpenCV already has two pre-trained face detection classifiers, which can readily be used in a program.

OpenCV is a Library which is used to carry out image processing using programming languages like python. This project utilizes OpenCV Library to make a Real-Time Face Detection using your webcam as a primary camera. This project uses LBPH (Local Binary Patterns Histograms) Algorithm to detect faces.