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 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 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 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 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.