AI face recognition is mainly achieved based on deep learning algorithms, especially convolutional neural networks. Now, let's talk about the detailed principles: I. Data Collection and Preprocessing Image Collection Use devices such as cameras to obtain face images. These devices can be security cameras, front cameras of mobile phones, etc. The quality of the collected images will affect the recognition effect. Factors like the resolution, clarity, and lighting conditions of the images are all very important. For example, in an access control system, the camera needs to capture clear front images of faces, avoiding blurry, too dark or too bright situations. Generally, it is required that the face occupies a certain proportion in the image and that key parts such as facial features are visible. Preprocessing The preprocessing stage includes operations such as grayscale conversion, normalization, and filtering on the images. Grayscale conversion is to convert a color image into a grayscale image, which can reduce the amount of data while retaining the basic contour and texture information of the image. Normalization is to normalize the pixel values of the image to a specific range. For example, normalizing the pixel value range from 0 - 255 to 0 - 1 makes it easier for the model to handle. The filtering operation is to remove noise in the image. For example, Gaussian filtering can smooth the image, reduce interference factors such as salt-and-pepper noise, improve the image quality, and make the subsequent feature extraction more accurate. II. Feature Extraction Application of Convolutional Neural Networks (CNN) CNN extracts the features of faces through convolutional layers and pooling layers. The convolutional kernels in the convolutional layer slide on the image to extract different local features. For example, a convolutional kernel can extract the edge features of a face, such as the contours of the eyes, nose, and mouth. Suppose a convolutional kernel is used to extract the contour feature of the eyes. It will slide on the image. When it slides to the eye area, since the pixel changes of the eye contour conform to the pattern of this convolutional kernel, a strong response will be generated, thus extracting the feature of the eye contour. The pooling layer downsamples the extracted features to reduce the amount of data. For example, max pooling will select the maximum value in a small area as the output, which can make the model have a certain robustness to small translations, rotations, and other changes of the face in the image. Deep Feature Representation After being processed by multiple convolutional layers and pooling layers, the network can extract the deep features of the face. These features are abstract and representative vectors. For example, a well-trained face recognition model can represent the face features as a 128-dimensional or 256-dimensional vector. The face feature vectors of different people have different distributions in this high-dimensional space. The feature vectors corresponding to different face images of the same person are relatively close in space, while the feature vectors of different people are relatively far apart. III. Classification and Recognition Training Classifiers Use a large amount of labeled face image data to train classifiers. The labeled data contains the identity information of different people. For example, one face image is labeled as "Zhang San", and another is labeled as "Li Si". During the training process, the model learns to associate the extracted face feature vectors with the corresponding identity labels. Commonly used classifiers include support vector machines (SVM), Softmax regression, etc. Taking Softmax regression as an example, it can calculate the probability that the input face feature vector belongs to each identity label. For example, for a dataset containing 100 face categories (100 different people), Softmax regression will output a 100-dimensional probability vector, where each dimension represents the probability that the face belongs to a specific person. Recognition and Matching In the recognition stage, when an input face image to be recognized is received, the model first extracts its feature vector and then calculates the probability that it may belong to each known identity through the classifier. Compare the calculated feature vector with the feature vectors already stored in the database. Distance measurement methods such as Euclidean distance and cosine distance can be adopted. If the distance between the feature vector of the face to be recognized and the feature vector of a certain person in the database is less than a set threshold, it is judged to be this person. For example, in a company's attendance system, the face feature vectors of employees are pre-stored in the database. When an employee clocks in, the system extracts the face features and compares them with the features in the database. If the matching is successful and the distance is within the threshold range, the employee is recognized and the attendance record is completed. Okay, we are Yiyun Technology. Follow us. You need a reliable team.