I. Introduction
Face detection is one of the important tasks in computer vision and is widely used in areas such as face recognition, facial expression analysis, and face tracking. Face detection in live video streams can help us quickly and accurately identify and locate faces in images. This article will introduce how to use the OpenCV library to obtain real-time video streams through a local camera and use a pre-trained deep learning model for face detection.
Deep learning models are increasingly used in computer vision, and face detection models are an important part of them. A face detection model based on convolutional neural network (CNN) will be used here to detect faces in images. The model has been trained with a large amount of face image data and can achieve high detection accuracy in various scenarios.
2. Convolutional Neural Network (CNN)
Convolutional Neural Network (CNN) is a special type of neural network specifically designed to process data with a grid structure, such as images and videos. Compared with traditional fully connected neural networks, CNN performs well in the field of image processing and is widely used in tasks such as image classification, target detection, and face recognition.
The core idea of CNN is to use the convolutional layer (Convolutional Layer) and the pooling layer (Pooling Layer) to extract features in the image, and perform tasks such as classification or regression through the fully connected layer (Fully Connected Layer).
The convolutional layer is the core component of CNN, which performs a convolution operation on the input image by sliding a small two-dimensional filter to extract features of local areas. These filters can learn different features, such as edges, textures, etc., and gradually acquire higher-level semantic features by extracting and combining local information of the image.
After the convolutional layer, a pooling layer is usually added to reduce the spatial size of the feature map and preserve key features. The pooling layer performs aggregation operations on local areas (such as maximum pooling, average pooling, etc.)