56results about How to "Improve face recognition rate" patented technology

The invention provides a face recognition method based on a convolutional neural network. The face recognition method comprises the following steps: carrying out necessary pretreatment at early stage on a facial image to obtain an ideal facial image; selecting the ideal facial image as the input of the convolutional neural network to enter U0, wherein the output of the U0 enters UG, and the output of the UG is taken as the input of US1; extracting edge components in different directions in an input image as first-time feature extraction and outputting to the input of special UC1 through supervised training by the S nerve cell of the US1; taking the output of the UC1 as the input of US2, completing the second-time feature extraction by the US2 and taking as the input of UC2; taking the output of the UC2 as the input of US3, and completing the third-time feature extraction by the US3 and taking as the input of UC3; taking the output of the UC3 as the input of US4, and obtaining the weight, threshold value and neuron plane number in each layer in a supervision competitive learning mode by the US4 and taking as the input of the UC4; taking the UC4 as the output layer of the network, and outputting the final mode recognition result of the network determined by the maximum output result of the US4. According to the face recognition method, the recognition rate of faces in complex scenarios can be improved.

The invention, which relates to the technical fields including pattern identification, image processing and computer vision and the like, provides a face identification method based on a wavelet multi-scale analysis and a local binary pattern (LTP). The method comprises the following steps: selecting an appropriate face image; carrying out a multi-scale wavelet analysis on a training image to obtain a first-level low frequency approximation image and a second-level low frequency approximation image; utilizing an LTP algorithm to carry out conversion on the low frequency approximation images to obtain LTP characteristic values of all pixel points; carrying out statistics on LTP histograms of the images by utilizing a blocking method and connecting the blocked histograms of the images of the two levels to obtain characteristic vector representation of the face image; and for a to-be-identified face, obtaining a characteristic vector of the to-be-identified face image and then using X <2> probability statistics to complete face identification. According to the method provided by the invention, an image noise effect can be effectively reduced; extraction capability for image texture characteristics can be enhanced; besides, the method has advantages of good robustness, high identification rate, fast calculating speed and important practical value.

The invention discloses a face recognition method based on a face image feature extreme learning machine. The method comprises steps as follows: preprocessing original images; performing principal component analysis on samples to obtain feature face spectrums, and projecting the images into a feature domain; then establishing the mapping relation between face images and face tags with an extreme learning machine algorithm; finally, deducting tag attributes input into the face images by using the extreme learning machine. According to the method, the advantages of the extreme learning machine are taken, the complexity of estimation and optimization of parameters of a traditional neural network is decreased, the training time is further shortened, and the recognition rate of the face images is further increased.

The invention provides a method for improving the human face identification rate. The method is suitable for a digital camera with a memory unit. Human face information is stored in the memory unit, and includes human face characteristics. The method for improving the human face identification rate comprises the following steps: retrieving a first human face image of a target human; executing a human face identification program to determine whether the first human face image is corresponding to human face information in the memory unit or not through the human face identification program; and executing an updating program when the first human face image is corresponding to the human face information so as to increase the human face characteristics corresponding to the target human in the memory unit.

The invention relates to a face identification method based on the manifold learning, which comprises the following steps: offering partial category relation saving the face data from the data partial structure; converting the optimum partial of every point to the total optimum with the arrangement technique; getting the data projection matrix from higher dimensional to lower dimensional by proceeding characterized dissociation for arrangement matrix, the original training sample and transposed product; mapping the identifying face image to the lower dimensional space; classifying with the nearest neighbor method in the lower dimensional space. The invention provides the higher face identification ratio, which provides higher identification ratio than the identification with principal component analytical method.

The invention provides an apparatus and method for registering plurality of facial images for face recognition. A facial image registration apparatus and method are provided for registering a plurality of facial images to be used for face recognition. A plurality of facial images are detected and sorted from a plurality of captured images input from a camera unit at the request of a user. A face feature value is calculated for each of the facial images. The face feature value is a score that is applied according to face features of each of the facial images. Facial images selected according to the calculated face feature values are registered as target facial images to be used for face recognition. As such, various facial images may be registered at a user's single request, thus improving a face recognition rate.

The invention provides a face recognition method and device, and relates to the technical fields of pattern recognition and biological feature recognition. The face recognition method and device can be used for carrying out effective recognition under various illumination conditions, and enhances the performance of face recognition. The method comprises the following steps: acquiring an original face test sample to be recognized, and carrying out Gaussian difference filter treatment on the original face test sample to obtain a filtered face test sample; comparing the original face test samplewith a prestored original face training sample set registered under normal illumination conditions, comparing the filtered face test sample with a prestored face training sample set subjected to Gaussian difference filter, and finding out the recognition object corresponding to the original face test sample from the original face training sample set; and computing to obtain an overall SCI (Sparsity Concentration Index) of the original face training sample of the recognition object and the filtered face training sample, and judging whether the original face test sample is a registered face according to the SCI.

The invention relates to a face recognition method based on an uncertainty quantization probability convolutional neural network. The method comprises a training stage and an identification stage. Theprobabilistic convolutional neural network is trained through samples with known categories. The extraction of face features is realized by a learning process of a probability convolutional neural network. The description of the face features is represented by the connection weight; testing the trained probability convolutional neural network by using a training sample, and determining a classification threshold. A to-be-identified sample is input into the probability convolutional neural network. An output vector of the probability convolutional neural network is calculated. A maximum component is compared with a classification threshold value to give an identification result. Uncertainty quantitative analysis is made on the identification result, and a mean value and variance estimationof the identification result are provided. The probability convolutional neural network system is adopted. The explicit feature extraction process is avoided through the difference extraction layer and the feature mapping layer. The feature which greatly contributes to the construction of the sample space is implicitly obtained from the sample. Compared with the prior art, the recognition rate and the anti-interference performance are higher.

The invention discloses a self-depth optimization face recognition device and a method thereof. The device comprises a shell and a standing plate located at the bottom end of the shell. A sliding groove is formed in the side, close to the standing plate, of the shell. A sliding block matched with the sliding groove is arranged in the sliding groove. A first motor is arranged at the bottom end in the shell, a lead screw extending into the sliding groove and penetrating through the sliding block is arranged at the output end of the first motor, a three-dimensional face recognition mechanism is arranged in the sliding block, and a distance sensor matched with the three-dimensional face recognition mechanism is arranged on one side of the shell. The beneficial effects are that the motor drivesthe screw rod to rotate to lift the three-dimensional face recognition mechanism to a proper height, so that the three-dimensional face recognition mechanism is suitable for people of different heights; the internal face recognition reference library can be dynamically updated based on a self-depth optimization algorithm according to a time period and a feature data change rate. Therefore, the face recognition rate of people whose faces are in a rapid growth and development or aging stage is greatly improved, and the application range and depth of the face recognition technology in differentpeople and different fields are greatly improved.

The invention relates to a sparse representation face identification method based on constrained sampling and shape features, belonging to the field of image processing. The method comprises the following steps: extracting the textural features and shape features of face images in a training set based on constrained regions to obtain a textural feature matrix and a shape feature matrix, wherein each category of the textural feature matrix and the shape feature matrix respectively corresponds to the multiple face images of one person in the training set; and comparing each category in the training set with the obtained textural feature vector and shape feature vector of the face image to be identified, and taking the category in the training set corresponding to the maximum comprehensive similarity value as the identification result of the person to be identified. The method has higher face identification rate.

The invention provides a face recognition method based on a convolutional neural network, comprising: performing necessary preprocessing on the face image to obtain an ideal face image; selecting the ideal face image as the input of the convolutional neural network Enter U0, the output of U0 enters UG, and the output of UG is used as the input of US1; the S neuron of US1 extracts the edge components in different directions in the input image through supervised training as the first feature extraction and outputs to the input of UC1 ;The output of UC1 is used as the input of US2, and US2 completes the second feature extraction and is used as the input of UC2; the output of UC2 is used as the input of US3, and US3 completes the third feature extraction and is used as the input of UC3; the output of UC3 is used as US4 US4 obtains the weights, thresholds and the number of neuron cell planes of each layer through supervised competitive learning as the input of UC4; UC4 is used as the output layer of the network, and outputs the final network determined by the maximum output result of US4 Pattern recognition results. The invention can improve the face recognition rate in complex scenes.

The invention discloses a face recognition method based on structuralized factor analysis. The problem that the face recognition accuracy rate is low due to the fact that local clustering features and a global distribution structure of face image data cannot be kept in the prior art is mainly solved. The face recognition method comprises the achieving steps of 1 dividing a face image data set; 2 performing clustering analysis on all training sample data; 3 calculating an optimal feature projection matrix through Gibbs sampling; 4 extracting low-dimension features of all test sample data and the training sample data; 5 recognizing a face image. The face recognition method synthesizes the local clustering features and the global distribution structure of the face image data and improves the face recognition accuracy rate.

The invention relates to a removing method for a glasses frame in a face image. The method comprises the steps of: detecting and positioning eyes in the input face image with a pair of glasses, geometrically correcting the face image on the basis of the positioned result to form a first image; extracting an eye estimation area from the first image to obtain a second image, and conducting mathematical morphology processing on the second image so as to determine the position of a glasses frame; based on the first image, establishing a face skin color model; in the second image, interpolating each pixel point of the glasses frame the position of which is determined, correcting the interpolation result in a part of pixel points of the glasses frame according to the skin color model, and conducting de-noising processing to form a ninth image; and replacing the eye estimation area in the first image by the ninth image, synthesizing and outputting the face image the glasses frame of which is removed. According to the technical scheme, the glasses frame in the face image with a pair of glasses can be conveniently and effectively removed, and the face recognition rate can be further improved.

The invention discloses a face recognition method for face images with lower occlusions. For face images to be recognized with lower occlusions and several face image samples without occlusions, feature images are extracted respectively. The extraction method is as follows: extracting and obtaining The image of the face area is input into the convolutional neural network, and the feature map output by the last layer of convolution layer is used as the initial feature map of the face image, and each initial feature map is multiplied by the weight matrix to obtain the feature image. The value of the weight element corresponding to the upper part of the initial feature map in the matrix is ​​larger, and the value of the weight element corresponding to the lower part of the initial feature map is smaller, and then calculate the feature image of the face image to be recognized and the weight of each face image sample The similarity of feature images completes face recognition. In the present invention, the area weight processing is performed on the feature map of the convolutional neural network, so as to strengthen the unoccluded feature and improve the face recognition rate of the lower occluded face image.

The invention discloses a selective integrated face recognition method based on genetic algorithm fusion differential evolution. Firstly, the HOG feature of the face image is extracted, and then the PCA algorithm is used to reduce the dimension of the face image, so as to reduce the computational complexity. Finally, use After dimensionality reduction, the GADESEN algorithm is used to classify and identify the data. This method takes the support vector machine as the base classifier, extracts N samples from the original training set with replacement, iterates T times according to this method, and uses the sample set generated each time to train the base classifier model. The classifier encodes real numbers to generate an initial population. In the mutation operation, the difference vector is used to guide the mutation and then produce high-quality individuals. The crossover operation uses the parent individual and the mutant individual to generate crossover individuals, which increases the diversity of individuals. Retention strategies for genetic evolution.