1. After detecting the face and locating the key feature points of the face, the main face area can be cropped, and after being preprocessed, the recognition algorithm of the back end is fed. The recognition algorithm completes the extraction of the facial features and compares them with the known faces of the inventory to complete the final classification. Our main work in this area includes: · LGBP-based face recognition method problem: Statistical learning has become the mainstream method in the field of face recognition, but practice shows that the method based on statistical learning often has the problem of “weak promotion abilityâ€. Especially in the case where the “attribute†of the image to be identified is unknown, it is more difficult to determine the adoption. What kind of training image to train the face model. In view of this, while studying statistical learning methods, we also consider a class of methods for non-statistical pattern recognition. Idea: For a given face image, the LGBP method first convolves it with a number of Gabor filters of different scales and directions (the convolution result is called a Gabor feature map) to obtain a multi-resolution transformed image. Then, each Gabor feature map is divided into several disjoint local spatial regions, and the luminance change patterns of the local neighborhood pixels are extracted for each region, and the spatial region histograms of these change patterns are extracted in each local spatial region. The histograms of all regions of all Gabor feature maps are concatenated into a high-dimensional feature histogram to encode the face image. The final face recognition is achieved by a similarity matching technique between histograms (such as histogram intersection). The comparison with the results of FERET97 on the FERET four face image test set is shown in the table below. It can be seen that the method has good recognition performance. Moreover, the LGBP method has the advantages of fast calculation speed, no need for large sample learning, and strong promotion ability. Table. Comparison of LGBP method and FERET'97 best result · Gabor feature selection and discriminant analysis method based on AdaBoost problem: Face description is one of the core problems of face recognition. The research practice of face recognition shows that multi-directional and multi-scale Gabor features are extracted from image data under the condition that the three-dimensional shape information of face is difficult to accurately acquire. The right choice. Typical methods for face recognition using Gabor features include the Elastic Graph Matching Method (EGM) and the Gabor Feature Discriminant Classification (GFC). In practical application, EGM needs to solve the problem of positioning of key feature points, and its speed is difficult to improve. GFC directly uses the PCA dimension reduction and discriminant analysis for the downsampled Gabor features, although this avoids the precise positioning of key feature points. The problem, but the down-sampling feature dimension is still high, and a simple downsampling strategy is likely to miss a lot of useful features. Summary: In view of the above problems, we consider how to effectively reduce the Gabor features, and apply the AdaBoost algorithm, which is currently receiving great attention, to the selection of Gabor features, which is used to extract the most favorable features for identification. The Gabor feature is AdaGabor feature), and finally the recognition is achieved by discriminant analysis of the AdaGabor feature (referred to as the AGFC recognition method). The comparison experiments on the CAS-PEAL and FERET face image libraries show that the AGFC method can not only greatly reduce the dimension of Gabor features, but also avoid the "dimensionality disaster problem" more effectively, reduce the computational complexity, and the recognition accuracy also has A big improvement. Comparing AGFC with EGM and GFC, it can be seen that both EGM and GFC are subjective choices for selecting facial features of several facial features to extract facial features, and our proposed AGFC method is automated by machine learning. Choose those Gabor features that are critical to distinguishing between different faces. See the differences and connections between the three as shown in the figure below. Comparison of three different face modeling methods · SV-based Kernel discriminant analysis method sv-KFD Summary: Support vector machine (SVM) and Kernel Fisher analysis are two different ways to solve the linear indivisible problem by using the kernel method. We combine the two. We first prove that the normal vector of SVM optimal classification surface has zero space property under the premise of the intra-class divergence matrix based on support vector. Based on this, we define the Kernelized Decision Boundary Feature Matrix (abbreviated as Kernelized Decision Boundary Feature Matrix). KDBFM), finally using the zero space-based Kernel Fisher method to calculate the projection space. We further propose a fusion of the mean vector difference and KDBFM to construct an extended decision boundary feature matrix (EKDBFM), and successfully apply these two methods to the field of face recognition, experiments in FERET and CAS-PEAL databases. The results show that the proposed method has better recognition performance than the traditional face recognition algorithm. · Face recognition method based on specific face subspace problem: Eigenface is one of the most famous algorithms in the field of face recognition. It essentially uses PCA to obtain the linear subspace of face image distribution. This space reflects the common features of all face image distribution from the perspective of optimal reconstruction. However, for recognition, such a feature does not necessarily facilitate recognition, and the recognition task requires features that are most likely to distinguish different faces. Summary: In the "feature face" method, everyone has a face subspace, and our method creates a face subspace that is private to the individual face, so that not only can better describe the faces of different individuals. The difference is, and the most likely to discard the intra-class differences and noise that are unfavorable for recognition, and thus has better discriminating ability than the traditional "feature face algorithm". In addition, for the face recognition problem of only a single training sample for each individual to be identified, we propose a technique for generating multiple training samples based on a single sample, so that an individual face subspace method requiring multiple training samples can be applied to Single training sample face recognition problem. The Yale Face DatabaseB face-to-face comparison experiment also shows that our proposed method has better recognition performance than the traditional eigenface method and template matching method for expression, illumination, and attitude changes within a certain range. 2. The human face recognition technology consists of three parts: (1) Human face detection Face detection refers to judging whether there is a face image in a dynamic scene and a complex background, and separating the face image. There are generally the following methods: 1 reference template method First, design one or several templates of standard faces, and then calculate the degree of matching between the samples collected by the test and the standard templates, and judge whether there is a human face through the threshold; 2 face rule method Since the face has a certain structural distribution feature, the method of the face rule is to extract these features to generate corresponding rules to determine whether the test sample contains a human face; 3 sample learning method This method adopts the method of artificial neural network in pattern recognition, that is, the classifier is generated by learning the face image sample set and the non-face image sample set; 4 skin color model method This method is based on the law that the skin color distribution is relatively concentrated in the color space. 5 feature sub-face method This method treats all face image sets as one face image subspace and determines whether there is a face image based on the distance between the detected sample and its projection between the child holes. It is worth mentioning that the above five methods can also be comprehensively adopted in the actual detection system. (2) Human body tracking Face tracking refers to dynamic target tracking of the detected face. Specifically, a model-based approach or a combination of motion and model is used. In addition, tracking with skin color models is a simple and effective means. (3) Comparison of human body appearance The face comparison is to identify the detected face image or perform a target search in the face image library. This actually means that the sampled image is compared with the stock image in turn and the best match is found. Therefore, the description of the image determines the specific method and performance of the face recognition. At present, two description methods of feature vector and face pattern template are mainly used: 1 eigenvector method The method first determines the size, position, distance and other attributes of the facial iris, nose, mouth angle and the like, and then calculates their geometric feature quantities, and these feature quantities form a feature vector describing the image. 2-sided stencil method The method is to store a number of standard face image templates or face image organ templates in the library. When performing the comparison, the sample face image is matched with all the pixels in the library using normalized correlation metrics. In addition, there is an autocorrelation network using pattern recognition or a combination of features and templates. The core of human face recognition technology is actually "local body feature analysis" and "graphic/neural recognition algorithm." This algorithm is a method that utilizes various organs and features of the human face. For example, the corresponding geometric relationship multi-data formation identification parameter is compared, judged and confirmed with all the original parameters in the database. Generally, the judgment time is less than 1 second. 3. The process of recognizing the human face Generally divided into three steps: (1) First establish a facial image of the human face. That is, the camera collects the facial image files of the human body of the unit personnel or takes their photos to form an image file, and stores these face image files to generate a faceprint code. (2) Get the current body image That is, the face image of the current entry and exit person captured by the camera, or taking a photo input, and generating the face image of the current face image file. (3) Aligning the current face code with the file inventory The face code of the current face image is searched and compared with the face code code in the file inventory. The above-mentioned "face code coding" method works according to the essential features and the beginning of the face of the human body. This face code is resistant to changes in light, skin tones, facial hair, hair, glasses, expressions and postures, and has a strong reliability that allows it to accurately identify someone from a million people. The process of recognizing the human face can be done automatically, continuously, and in real time using ordinary image processing equipment.
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