53 results about "Age estimation" patented technology

The invention relates to an age estimation method based on multi-dimensional data, in particular to a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment, a nonvolatile computer readable storage medium and application thereof. The method can more accurately estimate the age of Asian people, especially the age of East Asia or Southeast Asia, and can be used for evaluating senescence-related information.

The invention belongs to the field of biomedicine, particularly relates to an age estimation method based on proteome data, and more particularly relates to a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment and a nonvolatile computer readable storage medium. And their use for assessing the biological age of a subject, the degree or rate of aging of the subject, the degree or rate of aging of a specific tissue / organ of the subject, the effect of aging intervention means, the risk of the subject to suffer from an aging-related disease and / or the therapeutic effect of the aging-related disease.

The invention belongs to the field of biomedicine, particularly relates to an age estimation method based on metabolic data, and more particularly relates to a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment and a nonvolatile computer readable storage medium. And their use for assessing the biological age of a subject, the degree or rate of aging of a subject, the effect of an aging intervention means, the risk of a subject having an aging-related disease, and / or the therapeutic effect of an aging-related disease.

The present invention provides a method and a terminal for face age estimation under low-resolution images based on deep learning, including S1: preprocessing of image data; S2: extraction of image features; S3: aggregation of features at different levels; S4: age estimation module; The present invention is a feature extraction network designed specifically for low-resolution face images, and a multi-granularity pooling layer is designed for downsampling operations to reduce the loss of detailed information during downsampling; thereby enhancing the accuracy of face age estimation in low-resolution scenarios. The present invention performs multi-granularity downsampling on low-resolution face images, effectively improving the accuracy of age estimation under low-resolution images by retaining sufficient detailed information, achieving 2.3 MAE and 91.8% CS(5) accuracy at a resolution of 64×64.

The application provides a human body image age estimation method, system, medium and device, comprising: acquiring a human body image and a corresponding age label; training a human body image age estimation model based on the human body image and the age label, so as to estimate the age of the human body image based on the trained human body image age estimation model. The application uses a convolutional neural network as a backbone model to extract human body image age-related features, and adds a design scheme of a plurality of age stage classifier network structures that overlap with each other at the end of the backbone model; the learning of the model on different age difference characteristics is strengthened, the correlation between different ages is related through the mutual overlap between the age stages of each classifier, and the accuracy of the human body image age estimation model is effectively improved.

The invention relates to the technical field of deep learning and multi-modal feature fusion, and provides an age estimation method and device based on multi-modal feature fusion. Extracting an image feature vector of the face image by using an image encoder in the CLIP model, and extracting a text feature vector by using a text encoder in the CLIP model; splicing the image feature vector and the text feature vector to generate a fusion feature vector; inputting the fusion feature vector into a regression module for regression processing; and using the loss function to update trainable parameters of an image encoder and a text encoder in the attention fusion module, the regression module and the CLIP model. According to the method, multi-modal sample data is acquired, image features and gender text features are extracted by using a CLIP model, and a fusion feature vector is generated based on a weighted fusion mechanism, so that age information expression is realized; and the fusion features are input and predicted by the regression module, so that the age estimation accuracy is improved, and meanwhile, the method is also applied to various face recognition scenes.

The present invention relates to a multi-task gait recognition method and model based on long- and short-range time series extraction. The multi-task gait recognition method based on long- and short-range time series extraction described in the present invention dynamically excites a binarized gait sequence graph, and then excites it through horizontal pyramid mapping to obtain the characteristics of the excited gait sequence graph. Long- and short-range time series extraction is then used to help understand the microscopic details of the gait and provide a macroscopic gait context. This dynamic understanding on different time scales helps to construct more comprehensive and in-depth gait features, providing more complete feature data for the fully connected layer, and can effectively reduce the problem of long-term dependent information loss through the temporal convolution kernel, thereby improving the accuracy of human gait age estimation and gender estimation.

The application discloses a kind of family audio-video imprint automatic aggregation and personal biography film generation method and system based on genealogy figure. First, structured family knowledge base including family member node, blood relationship edge and associated multimedia data is constructed. In response to biography generation request, start multi-modal figure imprint aggregation: fusion face recognition and blood relationship auxiliary matching, automatically identify and extract all image fragments of target figure appearance from audio-video library, form cross-modal material set. Further, blood narrative guided intelligent editing is carried out: based on age estimation, material time sequence segmentation is carried out, narrative structure is automatically generated in combination with blood event node, and material enhancement technology is used to output into film. The system automatically generates the "family imprint" column in personal data card, and aggregates and displays related audio-video materials. Through integrated path, scattered family memories are gathered into personal image biography, which greatly reduces the production threshold, realizes the automatic aggregation and generation of family memories.

The application discloses a face image age estimation method based on a global-to-local ordinal regression network. The application comprises the following steps: step 1, preprocessing a data set; step 2, extracting features of face images in the preprocessed data set; step 3, local age domain division; performing k-means clustering analysis on the extracted feature data of the entire data set, and dividing the entire data set into multiple local data domains according to the clustering result; step 4, constructing a global regression network and a local regression network; and step 5, age estimation. The application solves the problem of insufficient precision of an existing deep ordinal regression network in a face age estimation task. Without increasing the complexity of network design, the application effectively improves the accuracy of the ordinal regression network for the face age estimation task. The application has the advantages of high recognition accuracy and strong robustness.

Relating to the estimation of the development month age of a child, the present invention obtains an estimation result with high objectivity regarding the development month age while increasing the degree of freedom of inspection items. [Solution] A development month age estimation device 3 comprises: a processor 15 for executing processing for estimating the development month age of a child which is the subject of inspection on the basis of inspection results for a plurality of inspection items; and a storage device 14 for storing data on a continuous probability distribution indicating a passage rate where month ages in a predetermined range are taken as a variable, for each of the plurality of inspection items. The processor 15 generates a development month age estimation function on the basis of data on a continuous probability distribution for two or more implementation inspection items used for inspection among the plurality of inspection items and an inspection result for each of the implementation inspection items, estimates the development month age of a child which is the subject of inspection on the basis of the mode value of a distribution of passage rates obtained by the development month age estimation function, and outputs an estimation result of the development month age.

Systems and methods for generating an age determination model based on a test data set of a test instrument. A control system of the computer-aided system is configured to use an age determination model to provide a change in instrument age of an operating instrument under its control. The control system may provide guidance to the user based on an age of the operating instrument and may perform one or more remedial actions when an updated instrument age of the operating instrument exceeds an age threshold.

To provide a diagnostic device that estimates a subject's age based on X-ray images.SOLUTION: In a diagnostic system in which a diagnostic device 101 is connected to a terminal via a communication network, the diagnostic device includes an image acquisition unit that acquires an X-ray image of a subject to be estimated, and an age estimation unit that estimates the age of the subject to be estimated, by setting the acquired X-ray image of the subject to be estimated as an input value, to a mathematical model that estimates age from an X-ray image.SELECTED DRAWING: Figure 2

The invention discloses a collaborative age estimation and gender classification method and system based on saliency map constraint. The method comprises the following steps: acquiring a training sample set, a verification sample set and a test sample set; performing filling, zooming and normalization processing on the image; performing iterative training on the adaptive scaling convolutional neural network; constructing a saliency map constraint training sample set, a saliency map constraint verification sample set and a saliency map constraint test sample set; obtaining a self-copying sample set and a mixed training sample set; constructing an adaptive scaling convolutional neural network based on saliency map constraint; x-ray skull localization side position films and copies of the X-ray skull localization side position films are spliced to serve as input data, and age estimation and gender classification results are obtained based on a self-adaptive scaling convolutional neural network constrained by a trained saliency map; according to the method, the accuracy and the stability of age estimation by using the X-ray image are improved, and the difference of age-increasing changes generated by development and aging of male and female bones is fully distinguished.

The present application relates to the technical field of geological disaster assessment, and provides a fault activity age estimation method, which comprises the following steps: obtaining the depth domain stratum data, the faulted state data and the dating sample data of a target fault, generating a stratum burial depth sequence and a depth-geological era corresponding relationship; determining an active burial depth constraint interval according to the stratum burial depth sequence and the faulted state data; calculating an active response parameter based on the sedimentary difference of the two plates of the target fault, dividing an active period and determining a period burial depth interval; converting the dating age into an age mapping burial depth interval, and matching the age mapping burial depth interval with the active burial depth constraint interval or the period burial depth interval to obtain an effective age anchor point; and finally generating a fault activity age genealogy chart. The present application takes the stratum burial depth sequence as a main control framework and takes the experimental dating data as evidence, so that the dependence on a single dating object can be reduced, and the misjudgment caused by the multi-solution of the dating data can be reduced.

The application discloses a local face age estimation method based on multi-channel learning in a shielding scene and relates to the technical field of age estimation. The application uses a method of simulating shielding, generates partially shielded face images and corresponding shielding labels based on a face age image dataset, trains a shielding detector to detect grids with shielding, proposes an age feature extraction model based on multi-channel learning, divides a to-be-detected image into grids, inputs the to-be-detected image into a feature extraction module, and extracts features of the grids. In combination with the shielding detector result, a feature fusion module is used to fuse features of the grids that are not damaged by shielding, obtain final age features, and further obtain an estimated age value. The application can accurately estimate the face age under a local shielding condition, meets the robustness and accuracy requirements of a face age estimation task in a local shielding scene, and solves the problem that the age estimation is inaccurate due to local shielding in the prior art.

The invention discloses an age estimation method based on a diffusion model, and relates to the technical field of face recognition. Comprising the following steps: constructing a face age estimation model, and inputting a data set into the face age estimation model; performing rearrangement marking and noise removal through a pairing comparison module to obtain a face age image; extracting features of the face age image through a trunk feature extractor, and decoupling the extracted features into additional variables; on the basis of the non-age features, Gaussian noise is gradually added to the face age image in multiple iterations through the forward process of a diffusion model, and a target image with noise is obtained; synthesizing the target image with noise and the age feature through a reverse process of a diffusion model to obtain an age corresponding to the face age image; and inputting a to-be-detected face image into the trained face age estimation model to obtain an age corresponding to the to-be-detected face image. The method can effectively separate and recombine age-related and irrelevant features, and improves the accuracy of face age estimation.

The present invention generally relates to age estimation. The invention provides a method and equipment for estimating age according to clothes. The method comprises the following steps: acquiring an image of a human; cutting one or more body parts from the image; segmenting a clothes image from the one or more body parts; background pixels are removed from the clothes image; the garment image is provided as an input to a neural network to obtain an output, where the neural network may be based on an 18-layer residual network. The output is indicative of an age category of the human. Garment-based age estimation provides an additional or alternative age estimation method, especially when the face is not visible in the image. The age estimation in the present invention uses machine learning to minimize deviations and analyze the background of the garment for better prediction. Age estimation may be used in a search engine to filter abuse child's content.

Systems, methods, and computer-readable storage media for age estimation / classification, and more particularly, for estimating / classifying the age of a person appearing within a video. A system configured as disclosed herein can receive a video and then identify a plurality of facial images of each person captured within the video. The system can create embedding of facial images and then cluster those images together based on the distance between the corresponding embedding. The system can also perform a matching degree algorithm on those facial images to determine which image provides the clearest image of the person (s), and then can estimate the age of the person (s) and / or send the best matching degree image of each person to a third party for analysis using the best matching degree image.

The invention discloses an age estimation network and method based on an elastic law and a non-uniformly distributed X-ray human skeleton image, and mainly solves the problem of insufficient optimization effect on a sample distribution imbalance task in medical image age estimation in the prior art. The network construction comprises the following steps: establishing a feature extractor for extracting batch features from an input image; introducing a feature queue, and using supervision force, distance elasticity and midpoint elasticity to do work on the samples so as to carry out comparative learning between the samples; defining the basic age estimation loss by using the supervision force, and obtaining the total loss of network iteration training; establishing a monotonicity constraint residual block for mapping the sample pair feature distance into a partial monotone increasing distance feature between samples; and establishing mutually independent age estimation regression heads for outputting an estimation result. According to the method, the performance of the automatic age estimation model on few sample data is improved, and the method has a good aging change characterization effect and can be used for disease risk assessment, individualized health management and medical research.

The embodiment of the invention discloses a method and device for estimating the age of a passenger in a vehicle cabin, electronic equipment and a storage medium. Comprising the following steps: acquiring a face image of a passenger in a vehicle cabin, and labeling a corresponding age label; face image data sets of different age groups are constructed according to the marked face images, and corresponding divergence features are constructed for age labels; cutting the face image according to a face detection algorithm and a face key point detection algorithm to determine face image frames of at least three sizes, and constructing an age estimation prediction model according to the face image frames of at least three sizes; training the age estimation and prediction model according to the face image data set and the age label until the total loss function of the age estimation and prediction model is converged; and performing age estimation on the passengers in the vehicle cabin according to the target age estimation prediction model obtained by training. By adopting the scheme, the accuracy and efficiency of estimating the age of the passenger in the vehicle cabin are improved.

To enable estimation of the age of a branch or its leaves of a citrus fruit tree with improved reliability compared to conventional methods, thereby reducing a mental burden on the worker and securing pruning accuracy.SOLUTION: A branch and leaf age estimation device 1 for estimating the age of a branch of a citrus fruit tree or the age of leaves on the branch is provided with a SPAD value calculation unit 504 and an age estimation unit 505. The SPAD value calculation unit 504 calculates a SPAD value representing a chlorophyll level of the leaf. The age estimation unit 505 estimates the age on the basis of the calculated SPAD value.SELECTED DRAWING: Figure 3

The invention provides a gender and age estimation method based on perception enhancement and a terminal. The method comprises the following steps: S1, preprocessing image data; s2, image feature perception enhancement; s3, extracting features of the trunk image; s4, performing multi-level feature fusion; s5, performing gender and age estimation; the method is a feature extraction network specially designed for a low-resolution face image, and the image feature size and the image detail information are increased through perception enhancement, so that the accuracy of face gender and age estimation in a low-resolution scene is enhanced. According to the method, the low-resolution human face image is subjected to perception enhancement, and the face detail information is increased, so that the gender and age estimation accuracy under the low-resolution image is improved.

Systems, methods, and computer-readable storage media for age estimation / classification, and more specifically to estimating / classifying the ages of people appearing within videos. Systems configured as disclosed herein can receive a video, then identify multiple facial images for each individual captured within the video. The system can create embeddings of the facial images, then cluster those images together based on distances between the corresponding embeddings. The system can also execute a matchability algorithm on those facial images, determining which of the images provides the clearest image of the individual(s), and can then estimate the age of the individual(s) using the best matchability images and / or send the best matchability image for each individual to a third party for analysis.

The invention discloses a core rot tree age estimation method based on a tree growth model and a probability theory thought, and the method is characterized in that the method comprises the following steps: (1) building a regional homologous growth model: building a regional homologous tree species growth equation cluster based on a plant morphological occurrence consistency principle; (2) radial growth data probability characterization: aiming at core rot sample wood, converting an accumulated radial growth amount sequence of a measurable annual ring section into a parameterized data set; and (3) Bayesian inversion and core rot tree age estimation: calculating a final age estimation value by a convergence parameter set based on parameter prior distribution constructed by a regional prior database. The method has the following characteristics: (1) spatial heterogeneity internalization: the influence of site conditions is automatically integrated, and a tedious factor separation step in a traditional method is avoided; and (2) the method breaks through the technical barrier of chronology reconstruction of the core-rot xylem by deeply coupling the growth model and the probability thought, and solves the problem of core-rot tree age estimation.

The invention relates to an age estimation method based on transcriptome sequencing, in particular to a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment, a nonvolatile computer readable storage medium and application thereof. A series of age-related immune cell proportion changes are identified by combining a single cell sequencing technology, and the age-related immune cell proportion changes can be used as a good aging marker.

An object of a system according to an embodiment is to accurately estimate the age of a pet and obtain information for providing appropriate care.SOLUTION: A system includes a photograph analysis unit, a question analysis unit, and an age estimation unit. The photograph analysis unit analyzes the photograph of the pet. The question analysis unit analyzes an answer to a simple question. The age estimation part integrates the analysis results of the photograph analysis part and the question analysis part to estimate the age of the pet.SELECTED DRAWING: Figure 1

The invention provides an age estimation method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of age estimation. The age estimation method comprises the following steps: acquiring basic speech features of a target object; based on a Titanet neural model and a residual block structure, performing feature optimization on the basic speech features to obtain a first embedded feature vector of the target object; and estimating the age of the target object based on the age segmentation point set, the recursive binary tree structure and the first embedded feature vector of the target object. The problem that the age recognition effect is poor due to dependence on basic acoustic features in the related technology is at least solved. The method is suitable for age estimation scenes.

The invention relates to an age estimation method based on methylation chip sequencing, in particular to a DNA methylation level data set, a data set establishing method, an age estimation model training method, an age estimation method, an age estimation model or device, electronic equipment, a nonvolatile computer readable storage medium and application thereof. By screening the Beta value of the methylation site related to the time sequence age and / or the biological age, the time sequence age and / or the biological age of the testee can be accurately predicted by utilizing an elastic network machine learning model.

A system, method, and computer-readable storage medium for age estimation / classification, more specifically for estimating / classifying the age of people visible in a video. The system configured to be disclosed herein can receive a video and then identify multiple facial images for each individual captured in the video. The system can create embeddings of the facial images and then cluster those images together based on the distance between the corresponding embeddings. The system can also run a resemblance algorithm on those facial images to determine which of the images provides the clearest image of the individual, then estimate the individual's age using the best-resemblance image, and / or transmit the best-resemblance image for each individual to a third party for analysis.

The invention discloses a lung organ age prediction method based on relation learning, and relates to the technical field of computers, and the method comprises the steps: S1, constructing an age prediction model; s2, acquiring a training data set; s3, training and optimizing the age prediction model to obtain an optimized age prediction model; s4, performing prediction by using the optimized age prediction model to obtain age relation estimation of each image; s5, analyzing the age estimation of each image; and S6, analyzing the age of the lung organ. The age prediction model can capture the overall aging characteristics of organs, then age relation prediction is carried out, and the biological age of the lung CT images in the test set is inferred according to the age relation prediction results of the multiple lung CT images; based on a 3D sample age inference strategy of a reference sample and an optimization method, through ablation experiments with other strategies and contrast experiments with other model frameworks, the superiority of the method in multiple evaluation indexes is proved, and the method can effectively improve the accuracy of lung age prediction.