The application of face recognition using convolutional neural networks is a hot research area, which can play an important role in security, entertainment, medical treatment and so on. A convolutional neural network is a deep learning model that can extract high-level features from an image and use a classifier to determine whether there is a face in the image and which person the face belongs to. This paper introduces an application method of face recognition based on convolutional neural network, which mainly includes the following steps:
1. Data preparation. First of all, a large amount of face image data needs to be collected, labeled and divided to form a training set, a verification set, and a test set. The training set is used to train the convolutional neural network model, the validation set is used to adjust the model parameters, and the test set is used to evaluate the model performance. The purpose of data preparation is to provide enough diverse and high-quality data so that the model can learn effective features.
2. Data preprocessing. Since the face image data may have different sizes, angles, illumination, occlusion and other problems, it is necessary to preprocess the data to improve the generalization ability and robustness of the model. Data preprocessing methods include cropping, scaling, rotating, flipping, grayscale, histogram equalization, normalization, etc.
3. Model building. According to the task requirements of face recognition, select the appropriate convolutional neural network structure, such as VGG, ResNet, MobileNet, etc., and adjust the number of layers, convolution kernel size, step size, and padding of the model according to the scale and complexity of the data set , activation function and other parameters. The purpose of model building is to build a network that can effectively extract and classify facial features.
4. Model training. Use the training set data to train the model, use stochastic gradient descent (SGD) or other optimization algorithms to update the model parameters, and use the cross-entropy loss function or other loss functions to measure the error of the model. At the same time, use the verification set data to monitor the training process of the model, and adjust hyperparameters such as learning rate, regularization coefficient, and batch size according to the accuracy rate or other indicators on the verification set. The purpose of model training is to enable the model to achieve high accuracy on the training set and avoid overfitting or underfitting on the validation set.
5. Model testing. Use the test set data to test the model, evaluate the generalization ability and recognition effect of the model on unknown data, and use the accuracy rate, recall rate, F1 value and other indicators to measure the model performance. At the same time, you can perform visual analysis on the model, observe the feature maps extracted by the model at different levels, and analyze which aspects the model performs well and which aspects are insufficient. The purpose of model testing is to verify whether the model has achieved the expected goal and provide a reference for subsequent improvement.