35 results about "Age classification" patented technology

Systems and methods for short message age classification in accordance with embodiments of the invention are disclosed. In one embodiment of the invention, classifying messages using a classifier includes determining keyword feature information for a message using the classifier, classifying the determined feature information using the classifier, and estimating user age using the classifier.

The invention discloses a face recognition method and system based on a deep convolutional neural network, and the method comprises a detection cutting step: receiving a face picture, obtaining the coordinates of key feature points of a face, and determining a cutting boundary according to the coordinates of the key feature points of the face based on a preset cutting rule; a feature extraction step: based on a backbone network of a face depth recognition model, obtaining a face feature vector with a fixed channel number according to the cut picture; an identity recognition step: matching theface feature vector, and obtaining identity recognition information corresponding to the face feature vector according to the similarity; and an age recognition step: according to the face feature vector, obtaining a recognition age based on an age recognition model of a plurality of age classifications. According to the method, the recognition processing speed can be improved, the face and age recognition accuracy is improved, and manpower and material resources are saved by reusing the face recognition model.

The embodiment of the invention discloses a face image age judgment method and device, computer equipment and a memory medium. The method comprises the following steps of obtaining to-be-judged face images; inputting the face images into a preset neural network model; and carrying out age classification on the face image according to classification data output by the neural network model. When theneural network model for carrying out face age judgment is trained, uniform expectation processing is carried out on the images of the same person photographed in different space environments, so a plurality of age expectations of different images of the same person are obtained; the age expectations are ranked and a median in a ranking result is taken out; and the age expectations of the different images of the same person in a training sample set are the same, so according to the neural network model trained and converged through utilization of the images, the age score output stability forthe same person in the different environments is high, and an age score is not liable to be influenced by the environments.

The invention provides a Microblog-based user age classification method and a Microblog-based user age classification system. The Microblog-based user age classification method includes the following steps that a user to be tested is obtained, and the user to be tested is a Microblog user who does not provide age information; a Microblog text of the user to be tested is obtained; the Microblog text of the user to be tested is segmented, so that text phrases to be tested are obtained, and according to a preset rule, the text phrases to be tested are converted, so that a feature vector to be tested is obtained; the feature vector to be tested is adopted as an input value of a pre-created maximum entropy classifier, so that a testing result is obtained; the testing result is utilized to determine the user age type of the user to be tested. By obtaining the Microblog text of the user to be tested, after carrying out related processing on the Microblog text, the application utilizes the maximum entropy classifier to obtain the testing result, and determines the user age type of the user to be tested according to the testing result, consequently, utilizing the maximum entropy classifier to carry out testing with the Microblog text published by the user to be tested as a basis can ensure that the determined user age type of the user to be tested is more accurate, and moreover, universality can be realized.

The invention discloses a content timeliness determination method based on an artificial intelligence technology, and the method comprises the steps: obtaining a multi-modal information set of target content, wherein the multi-modal information set comprises at least two of first text information, image information, auxiliary text information and audio information; performing feature extraction on the multi-modal information set to obtain a multi-modal feature set; based on the multi-modal feature set, acquiring a target probability distribution vector through an aging classification network, wherein the target probability distribution vector comprises P probability values, and each probability value in the P probability values corresponds to one aging label; and determining a target aging label corresponding to the target content according to the target probability distribution vector. The invention further discloses a device, equipment and a storage medium. The method can make full use of the features of multiple signal sources in the content to carry out timeliness prediction, thereby improving the accuracy of timeliness classification.

The invention discloses a mobile terminal face age editing method based on a StyleGAN. The method comprises the steps of S1 preparing a face picture training data set, and labeling part of face pictures in the face picture training data set to obtain age labels; S2 training a feature extraction model and a generation model by using the face picture training data set; S3 training a face age classifier by utilizing partial data in the face picture training data set and the feature extraction model; S4 selecting a resolution ratio and a channel coefficient according to user configuration and expected waiting time, and inputting the resolution ratio and the channel coefficient into the generation model to obtain a sub-model; S5 extracting a coefficient of the face age classifier, namely an attribute vector capable of judging the face age; and S6 editing the to-be-edited face picture by using the sub-model and the attribute vector. According to the technical scheme, deployment and maintenance cost can be reduced, user data security is enhanced, personal privacy leakage is avoided, dependence of software on a network is removed, and a user can use the software anytime and anywhere.

The invention relates to a vehicle riding object classification method and apparatus, a computer device and a storage medium. The method comprises the steps of obtaining a to-be-detected traffic picture; inputting the traffic picture into a target detection network to obtain a co-driver area of the target vehicle in the traffic picture; a face detection network is adopted to detect the co-driver area, and if it is detected that a riding object exists in the co-driver area, the face area of the riding object is determined; adopting an age classification network to identify the face area of theriding object to obtain a classification result of the riding object; wherein the classification result is used for representing whether the riding object is a child. According to the method, the classification process of the riding objects is completed by the computer equipment, so that the waste of human resources is greatly reduced, the detection efficiency is greatly improved, the computer equipment can detect all the acquired traffic pictures, and the omission ratio of the target vehicle is reduced; furthermore, a neural network model is adopted for detection processing, and the accuracyof a riding object classification result is also improved.

The invention belongs to the technical field of image classification, and particularly relates to a generative feature ordered regularization method for a depth ordered regression model. The method comprises the following steps: mapping an input image into low-dimensional feature representation through a deep convolutional neural network, and calculating the distance between the low-dimensional feature representations of all samples in a batch in a corresponding low-dimensional space; then calculating the distance between the ordered labels of all the samples in the batch; normalizing feature representation distance vectors and label distance vectors between all samples and other samples in the obtained batch; the divergence of the normalized feature vector and the tag distance vector is calculated, so that the distribution of the features in the embedding space is restrained to be consistent with the distribution of the ordered tags, namely, the orderliness of the features is ensured; the final loss function of the model comprises ordered regression loss and KL divergence loss; according to the method, the classification performance under various task scenes (such as face age estimation, medical image classification, historical image age classification and the like) can be improved.

The invention provides an age estimation method and device, electronic equipment and a computer readable storage medium to improve the accuracy of age estimation. The method comprises the steps that a target neural network receives a target image; the target neural network calculates a classification loss value according to the distance between the real age value and each age classification, the probability value corresponding to the real age and the probability value of each age classification in the training process, and performs parameter adjustment based on the classification loss value; wherein the target image is a mixed face image obtained by mixing different face images according to a preset proportion; the target neural network extracts a feature vector from the target image; the target neural network calculates a probability value corresponding to each age category based on the feature vector; the product of the probability value is added corresponding to each age category and the age deviation value to obtain an age prediction value; wherein the age deviation value corresponding to each age category is predicted in the training process, and the age deviation value belongs to a parameter.

The embodiment of the invention relates to the technical field of face image recognition, and discloses an age recognition model training method, a face age recognition method and a related device, and an age recognition model obtained through training comprises an age classification model, an uncertainty model and an age regression model. In the first stage, an uncertainty model is adopted to convert the influence of noise interference on a first predicted age value output by an age classification model obtained by training into an uncertainty probability. And in the second stage, the training set is adopted to train the regression neural network, and the uncertainty probability corresponding to each face image is introduced into a loss function to obtain an age regression model. The introduction of the uncertainty probability is equivalent to telling an age regression model: the extracted features of each face image have certain probability uncertainty. Therefore, in the training process, the image characteristics with high uncertainty probability need to be learned emphatically, the influence caused by the noise can be reduced, and the generalization ability and accuracy of the model are improved.

The invention discloses an electronic device, an image-based age classification method and a storage medium. The method comprises the steps of after receiving the first face image of a to-be-identified age, cutting the first face image by adopting a preset cutting rule and obtaining a preset number of second face images; respectively utilizing a pre-determined face image recognition model to perform age recognition on the second face images, and generating an age feature vector corresponding to each second face image; performing averaging treatment on each age feature vector to obtain an average age feature vector; carrying out age classification analysis on the average age feature vector by adopting a pre-determined age classification function, and obtaining an average age type corresponding to each second face image, wherein the average age type is an age type corresponding to the first face image. In this way, the problem that the training process of a face recognition model is large in calculation amount and low in efficiency in the prior art can be effectively avoided. The age classification accuracy based on face images can be improved.

The invention provides an age estimation method, an age estimation model training method and an age estimation model training device. The age estimation method comprises the following steps: acquiringa to-be-processed image containing a target object; inputting the to-be-processed image into a pre-trained age estimation model; outputting an age classification result corresponding to the to-be-processed image through a classification network of the age estimation model, and outputting an age regression result corresponding to the to-be-processed image through a regression network of the age estimation model; and determining the age of the target object based on the age classification result and the age regression result. According to the method, when the age of the target object is estimated through the age estimation model, the to-be-processed image can be classified to obtain the age classification result, the to-be-processed image is subjected to regression processing to obtain theage regression result, and the age of the target object is determined according to the age classification result and the age regression result. The classification and regression integrated mode improves the precision of age estimation.

The invention provides an integrated age detection system based on weighted kernel regression and encapsulated type deviation search. The system is characterized in that the system comprises a data collection device, an actual age input device, a memory, a preprocessing module, a feature compression module, a traditional age estimation module, a pathological age estimation module, a weighted kernel regression module, and a result output module. With the integrated age detection system adopted, the shortcomings of traditional age estimation methods and pathological age estimation methods are eliminated; and age classification estimation ability can be improved, and at the same time, the actual age estimation error of normal people can be effectively controlled; the algorithm of the whole framework of the system is clear in principle; the system is convenient to implement; the system has a better scientific basis for the detection of the ages and health statuses of healthy people or patients; and the system has high reliability and high feasibility.

The invention relates to a face age recognition method, device and system, an electronic device and a storage medium, and the method comprises the steps of carrying out the face detection of a collected image, and obtaining a face frame; performing feature point detection on the face in the face frame, and judging the facial expression according to the detected feature points; under the condition that the facial expression meets the recognition condition, judging the age classification of the face in the collected image through the age recognition model, so that the problem that the accuracy of age recognition through the face is low is solved, and the accuracy and reliability of age recognition only through the face are improved.