44 results about "Aging face" patented technology

The invention discloses a cross-age face verify method based on characteristic learning; the method comprises the following steps: 1, obtaining to-be compared two face images; 2, using a face characteristic point positioning method to carry out align operation for the two face images; 3, respectively carrying out feature extraction for each image, wherein the extraction method includes the following steps: a, automatically extracting high-level meaning characteristics through a depth convolution nerve network; b, calculating LBP histogram characteristics of the image; c, fusing the characteristics obtained in the a and b steps, and expressing characteristic vectors; 4, using a cosine similarity method to calculate a distance between the characteristic vectors obtained by the step3, and determining whether the two images are from a same person or not. The method firstly uses the depth network to cross-age face verify, and creatively combines the handwork design LBP histogram characteristics with depth network autonomous learning characteristics, thus realizing complementation between high-rise meaning characteristic and lower characteristics, and providing better accuracy.

The invention provides a method for building a deep learning based face recognition and age synthesis joint model. The method is characterized in that alignment and PCA and LDA dimension reduction preprocessing are performed on a pair of input images; six groups of features for identity representation and different age group representation are acquired through an automatic encoder acquired by training, an image similarity degree is outputted for each of the six results through a parallel CNN, then a matching result is acquired through weighted fusion. The method provided by the invention has an excellent effect for independent face recognition or age detection or a common task, and can also acquire an excellent effect for face recognition under the influence of illumination and postures; and the method also has robustness for cross-age face recognition because features of the age and the face identity are separated. In addition, some parameters and weights can be adjusted according to requirements, so that the method is very flexible.

The invention provides a model training method, a cross-age face recognition method and corresponding devices, which are used for improving the accuracy rate of cross-age face recognition, and reducing the complexity of cross-age face recognition. The model training method comprises the steps of: acquiring a universal face database and a cross-age face database, wherein the cross-age face database comprises a plurality of face image groups classified according to identity characteristics and age characteristics of faces; and training a deep convolution neural network model guided by a latent factor analysis model by using the universal face database and the cross-age face database, and outputting the deep convolution neural network model after training is finished.

The invention belongs to the field of image processing, and discloses an establishment method for establishing a bionic human facial aging model. The method comprises the steps of establishing a human face database, pre-processing an image in the database, extracting a feature point according to the modified ASM algorithm, starting a training program, obtaining the variation mapping between samples in a training library, respectively establishing a shape aging model and a texture aging model, starting a matching program, matching optimal samples for an input human face, selecting the aging age of a target human face, matching the shape aging model and the texture aging model, and synthesizing a final aging face. According to the technical scheme of the invention, the adaptive aging distortion of a human face image is realized. Meanwhile, the image matching is conducted based on the high-dimensional space image geometric method, wherein the texture information and the skin color information are separated. Therefore, the finally distorted image is more vivid.

The invention belongs to the field of daily chemical articles and discloses anti-aging face cream. The anti-aging face cream is prepared from isopropyl myristate, isohexadecane, lanolin, propylene glycol, hydrolyzed wheat protein, tartaric acid, calendula extract, camellia oleifera leaf extract, jojoba oil, allantoin, an epidermal growth factor, ginseng extract, glyceryl stearate, sorbitol, a lactobacillus fermentation product, shea butter, aloe extract, and grape seed extract. The anti-aging face cream has effects of maintaining beauty, resisting aging, promoting metabolism, activating skin cells, improving blood circulation, moistening skin and effectively preserving moisture.

The invention relates to cortex mori anti-aging face cream. Each part of face cream comprises the following components: olive oil, apricot kernel oil, glyceryl monooleate, sorbitan sesquistearate, glycerol alpha-tocopheryl, cortex mori extracts, hyaluronic acid, essence, methyl p-hydroxybenzoate and refined water; and after a face is washed, the cortex mori anti-aging face cream is directly applied to the face. The cortex mori anti-aging face cream can resist skin aging and slow down wrinkles and colored patches on the face skin and is not used with astringent containing alcohol components.

The invention provides a cross-age face recognition method. According to the method, a cross-age face recognition system composed of two modules (a maximum entropy feature description module and an aging perception de-noising automatic coding module) is obtained by training a large amount of face images including four age groups, thereby realizing recognition on any two different age face images. The maximum entropy feature description module allocates codes including maximum information amount by using maximum entropy splitting of a decision tree, the aging perception de-noising automatic coding module reconstructs a feature descriptor of any age group into feature descriptors of four different age groups, the descriptors are integrated to obtain a face integrated feature vector for eliminating aging influencing, and face recognition is finally realized by calculating the cosine distance of integrated feature vectors of different faces. The method can well reduce the information loss problem of some traditional descriptors, eliminates the influence of the aging factor in cross-age face recognition, and has good performance in the cross-age face recognition problem.

The invention discloses an anti-ageing face cream composition. The anti-ageing face cream composition contains nonapeptide-1, copper tripeptide-1, acetyl octapeptide-3 and palmitoyl tripeptide-5, wherein nonapeptide-1 can fade stains, brighten skin color and improve dark complexion; copper tripeptide-1 can reduce light damage and stain, brighten skin color, astringe loose skin, improve the elasticity, density, firmness and clarity of skin, decrease fine creases and deep wrinkles and greatly increase the proliferation of keratinocytes; acetyl octapeptide-3 can reduce deep wrinkles caused by contraction of facial mimetic muscles, especially wrinkles around the forehead and eyes; and palmitoyl tripeptide-5 can stimulate collagen synthesis in human fibrocytes and reduces any type of wrinkles. Through combined usage of the above components, the anti-aging face cream composition can significantly improve dull yellow skin color and fine creases just appearing on skin, fades and reduces long-lasting deep laugh lines, dry lines, nasolabial folds, etc., enhances early-stage aged and already aged skin, and has obvious anti-aging effect.

The embodiment of the invention provides a cross-age face recognition method and device. The method comprises the following steps: inputting a to-be-recognized face picture into a picture generation model, and obtaining generated pictures of a plurality of age groups corresponding to the to-be-recognized face picture; respectively inputting the to-be-recognized face picture and each generated picture into the basic feature extraction model and the feature extraction model of each age group, and outputting basic face features and face features of each age group; obtaining a feature vector according to the basic face features and the face features of each age group; and obtaining a recognition result of the to-be-recognized face picture according to the feature vector and a pre-obtained facelibrary. The embodiment of the invention provides a cross-age face recognition method and device. According to the cross-age face recognition method and device, the faces of different age groups aregenerated, the features of the faces in the face picture to be recognized and the generated features of the faces of different age groups are extracted respectively and fused, face recognition is conducted according to the fused face features, and the recognition accuracy of cross-age face recognition can be improved.

The invention provides a cross-aging face recognition method based on a unified generation model, which belongs to the technical field of computer vision and machine learning, and relates to the cross-aging recognition problem in face recognition. The method first assumes that the facial identity feature extracted from depth network can not completely eliminate the aging information on human face,and then the missing age pictures in the dictionary library are filled in by using the generation method, then the feature of each picture is extracted by face recognition network, and then the relevant image features are fused, and then the cosine similarity between the feature of each picture to be tested and each feature in the dictionary library is obtained. Finally, the object with the largest cosine similarity is taken as the correct matching object. The method of the invention can be used for cross-aging face recognition under various scenes.

The present invention belongs to the technical field of cosmetics, and particularly relates to a whitening and anti-aging face cleaner and a preparation method thereof, wherein the whitening and anti-aging face cleaner comprises lauric acid, glycerol, potassium hydroxide, squalane, sodium lauroyl glutamate, glyceryl monostearate, polyethylene glycol, a eucommia ulmoides extract, a horseradish tree leaf extract, a ophiopogon japonicus extract, a ginkgo biloba leaf extract, EDTA disodium, methylparaben, essence and water. The whitening and anti-aging face cleaner of the present invention has characteristics of mildness, good skin affinity, safety and no stimulating, and further has effects of deep skin cleaning, blood circulation promoting, moisturizing, whitening, and aging delaying.

The embodiment of the invention provides a cross-age face recognition method and equipment, and the method comprises the steps of determining a face feature vector, and decomposing the face feature vector into a first feature parameter and a second feature parameter, wherein the first feature parameter is not influenced by the age change of the face, and the second feature parameter changes along with the age change of the face; establishing a cross-age face recognition model according to the face feature vector, the first feature parameter and the second feature parameter; training the cross-age face recognition model based on face feature vectors and ages corresponding to face images in a cross-age face database to obtain a target cross-age face recognition model; inputting the face feature vector of the to-be-recognized face image into the target cross-age face recognition model, determining whether the target face image matched with the to-be-recognized face image exists in the cross-age face database or not and the identification information of the to-be-recognized face image, so that the recognition accuracy of cross-age face recognition is effectively improved.

The invention discloses a cross-age face recognition method and system based on ternary constraints, and belongs to the technical field of computer vision. According to the scheme, the method comprises steps of obtaining face sample data of different age spans, constructing an estimation model of age attributes, and estimating age attribute information for a training sample set, dividing three time units, respectively performing feature extraction on subsets of different age groups, taking the center features of different age groups of each identity object as a basis, continuously performing close constraint on the center features through features obtained by ternary sample set training, so that the feature aggregation effect of different age groups is improved, the final output result can be directly applied to a face recognition system. The features of different age groups are aggregated to obtain face features with finer granularity, and the problem that an existing face recognition method is low in recognition rate in cross-age-group face recognition can be effectively solved.

The invention discloses a cross-age face recognition model, method, system and device, and a storage medium. The method comprises the following steps: obtaining a face image pair; inputting the face image pair into a cross-age face recognition model for comparison, and outputting a comparison result; wherein the cross-age face recognition model comprises an age discrimination network, the age discrimination network comprises an age discrimination module and a feature generator, and the cross-age face recognition model is obtained by alternately updating the age discrimination module and the feature generator according to a related loss function. The method not only improves the age discrimination capability of the age discrimination module, but also improves the capability of the feature generator for obtaining the face features which enable the age discrimination module not to carry out age discrimination, thereby improving the extraction capability of the feature generator for the identity features, reducing the impact on the identity features from the age information, and improving the user experience. The method can be widely applied to the technical field of deep learning.

The invention relates to an anti-aging face-beautifying composition containing resveratrol. The composition comprises the following components: the resveratrol, a cranberry extract and a rose flower extract. The invention also provides a preparation of the composition. The preparation consists of the following components in parts by weight: 5-20 parts of the resveratrol, 3-6 parts of the cranberry extract, 3-6 parts of the rose flower extract, 6-10 parts of anhydrous citric acid, 6-10 parts of tartaric acid and 60-75 parts of prebiotics. Experiments show that a volunteer taking the composition provided by the invention is obviously improved in skin aging characteristics (the skin glossiness is obviously increased, and the skin condition is obviously improved), and the effective rate is above 75%.

The invention discloses a face age synthesis method for weak label data. The method belongs to the field of computer science and technology artificial intelligence, and comprises the specific steps ofhuman face feature extraction, human face age synthesis and identity information enhancement. According to the method, the problems of inaccurate data set labels and lack of matching age group face pictures can be solved, and meanwhile, more personal facial feature information is reserved due to the addition of the loop network, so that the generated regression/aging face effect is better; in addition, a good generation effect can still be ensured on the basis of solving the problem of insufficient data sets; the problem of data scarcity in a traditional face age synthesis task is solved, andthe method is widely applied to real life, such as missing population searching, movie and television entertainment, public welfare project propaganda and the like.

The invention discloses a cross-age face recognition method based on feature fusion, and the method comprises the steps: extracting eye shallow stable features of a face, extracting age-independent deep identity features through an orthogonal decomposition and joint learning mode, carrying out the fusion and dimension reduction of the two types of features in a full connection layer, and achieving the classification based on an Arcface loss function. According to the method, the LBP features of the eye region of the human face are added into the features for recognition, self-quotient enhancement is carried out on the eye region, and discrete wavelet transform noise reduction is carried out on the enhanced picture. Therefore, the obtained features are kept stable along with the change of age, and the robustness of LBP local binary mode coding on factors such as illumination in the picture is also high.

The invention relates to the technical field of artificial intelligence, and provides a cross-age face recognition model training method, a cross-age face recognition method and a cross-age face recognition device. The method comprises the steps that a training face image set is acquired, and the training face image set at least comprises a first face image and a second face image which belong to the same person at different ages and a third face image of another person; performing feature extraction on the training face image set to obtain a first age feature vector, a second age feature vector and a third age feature vector; calculating an age feature total loss value according to the first, second and third age feature vectors and a standard age feature vector; calculating a cross-age face feature loss value according to the first, second and third age feature vectors; and iteratively updating the basic recognition model according to the age feature total loss value and the cross-age face feature loss value until a preset iteration termination condition is reached, and obtaining a final cross-age face recognition model. The final cross-age face recognition model is good in recognition effect, and the recognition precision is obviously improved.

The invention discloses a cross-age face recognition method based on an attention mechanism and data imbalance. The method comprises the following steps: firstly, extracting facial photos of different ages of the same person from a cross-age face recognition data set to form a face time sequence, and processing through a self-attention mechanism to obtain a processed feature sequence; performing linear fusion on the feature sequence processed by the self-attention mechanism; decomposing the fused facial features into identity features and age features; and supervising the age estimation task and the identity estimation task by adopting the reweighted age loss and identity loss, and constraining the similarity between the age feature and the identity feature by adopting the correlation loss. According to the method, facial photos of different ages of the same identity and a self-attention mechanism are fused, the influence of the ages on the face is learned, and more robust identity features are extracted; by re-weighting the age loss, the accuracy reduction caused by age data distribution imbalance is relieved.

The invention discloses an anti-aging face cream. The anti-aging face cream is prepared from the following components in percentage by mass: 2-4% of collagen, 3-5% of echidnotoxin peptide, 10-20% of red wine, 0.3-0.5% of ceramide, 8-13% of grape seed oil, 1-3% of B-glucan, 0.8-1.7% of hyaluronic acid, and the balance of weak alkaline water. The anti-aging face cream has the efficacies of comprehensively enhancing the cell viability and elasticity, enhancing the skin compactness, and eliminating the dark metabolites on the epidermis, so as to achieve the anti-ageing purpose.

The invention relates to a face detection method, in particular to a face detection method based on a child cross-age face recognition technology, which comprises the following steps: acquiring a training face database and an age face database, extracting face basic features in the training face database, and inputting the face basic features into a double-layer heterogeneous deep neural network for training; and extracting face features of the same person at different ages and ages corresponding to the face features in the age face database, inputting the face features and the ages corresponding to the face features into a double-layer heterogeneous deep neural network for training to obtain a spatial mapping relationship of the face features at different ages, and processing at least twodifferent to-be-recognized face images to obtain a face recognition result; inputting the to-be-recognized face image into the trained double-layer heterogeneous deep neural network, and judging whether the face images are the same person or not by the double-layer heterogeneous deep neural network according to the similarity of the face features extracted from the to-be-recognized face image; according to the technical scheme provided by the invention, the defect that the cross-age face cannot be quickly and effectively recognized in the prior art can be effectively overcome.