37 results about "Score fusion" patented technology

The invention discloses a method and a system for detecting voice spoofing attack of speakers on the basis of deep learning. The method includes constructing audio-frequency training sets, initializing deep feed-forward neural networks and deep recurrent neural networks and respectively training the deep feed-forward neural networks and the deep recurrent neural networks by the aid of multi-frame feature vectors and single-frame vector sequences of the training sets; respectively leading frame level and sequence level feature vectors of to-be-tested audio frequencies into two trained linear differential analysis models in test phases, weighting two obtained result grades to obtain scores and comparing the scores to predefined threshold values so as to discriminate voice spoofing. The method and the system have the advantages that local features can be captured, and global information can be grasped; the linear differential analysis models are used as classifiers in identification and verification phases, the voice spoofing attack can be judged by means of grade fusion, and accordingly the voice spoofing detection accuracy can be greatly improved.

The invention discloses a face recognition method and system based on adaptive score fusion and deep learning. The system comprises a near infrared face and visible light face acquisition unit for jointly acquiring pictures of the same face by cooperation of cameras of two modes, a face feature extraction unit for respectively extracting features from the acquired near infrared face picture and visible light face picture via a deep convolution model, an adaptive score fusion unit for solving the similarity (that is, the score of the pictures) of the two pictures via an Euclidean distance and then fusing the face features extracted by using a deep learning algorithm via an adaptive score fusion algorithm on the score level to solve the final score of the user, and a result output unit for outputting the final score. The method and the system can solve the influence of the intensity of near infrared light on face imaging, and can also solve the problem that the details of the near infrared picture disappear by using the visible light picture.

The invention discloses a voiceprint recognition method based on gender, nationality and emotion information. The method specifically comprises the following steps: firstly, data preprocessing, secondly, feature extraction, and secondly, neural network parameter training: according to the specific structure of a neural network, in the training process, the input sequence of training sentences is disrupted, then 128 sentences are randomly selected as a training batch, and the number of data iterations is 80; wherein a training file required by the scoring fusion tool is a development set resultand a test set result of each system; wherein VOXCELEB1 test is used in the test set; wherein the development set is a test file which is generated based on 1211 training speaker statements and comprises 40,000 test pairs; and a final test set scoring result is obtained through 100 times of iteration. According to the invention, the recognition rate is improved.

A score fusion method and apparatus. The score fusion method includes receiving a plurality of scores respectively obtained by a plurality of classifiers, and fusing the received scores using a likelihood ratio. The score fusion apparatus includes a fusion unit which receives a plurality of scores respectively obtained by a plurality of classifiers and fuses the received scores using a likelihood ratio, and a parameter providing unit which provides the fusion unit with a plurality of parameters needed for fusing the received scores.

The invention relates to the technical field of computer vision, in particular to a face attribute classification system based on a bidirectional Ladder structure, which aims to solve the problem of how to fully utilize characteristics of different levels in a deep network and corresponding relations between characteristics of different levels and different face attributes so as to improve the accuracy of face attribute classification. For this purpose, the face attribute classification system based on the bidirectional Ladder structure provided by the invention comprises a bidirectional Ladder self-encoder module, a self-adaptive attention module and a self-adaptive score fusion module, the bidirectional Ladder auto-encoder module comprises an encoder module and a decoder module, the self-adaptive attention module comprises a plurality of attention sub-modules, and the self-adaptive score fusion module is configured to obtain a face attribute classification result of the face image tobe detected according to an output result of the encoder module and a result output by the attention sub-module. Based on the structure, the coding features and the decoding features of different levels can be fully utilized, and the accuracy of face attribute classification is improved.

Various embodiments of the present invention provide systems and methods for determining a personalized fusion score. In certain embodiments, the systems and methods are configured for calculating preliminary fused scores for consumers at least in part by applying a first score fusion technique across the sample of consumer data. Segmentation scores are then calculated based at least in part upon the preliminary fused scores. In those and other embodiments, the segmentation scores enable creation of a plurality of cluster subsets within the sample of consumer data. In certain embodiments cluster subsets are defined at least in part by a particular score mix, while in other embodiments subsets are defined at least in part by respective score fusion techniques that prove optimal for each subset. Further, in various embodiments, application of multiple score fusion techniques across respective cluster subsets provides personalized fusion scores for the consumers in each respective cluster subset.

The invention discloses a comparison score fusion method and device for multi-modal biological recognition, a medium and equipment, and belongs to the field of biological recognition. The method comprises the following steps: acquiring comparison scores of various biological features; normalizing the comparison score of each biological feature according to a plurality of set thresholds, wherein the plurality of thresholds are corresponding comparison thresholds when the FAR of the biological feature on the training set is a plurality of specific values; and performing weighted summation on the normalized comparison score according to a weighting coefficient to obtain a fused comparison score, wherein the weighting coefficient of the comparison score of each biological feature is calculated and obtained according to the corresponding FRR when the FAR of the biological feature on the training set is a plurality of specific values. According to the method and device, high precision can be achieved on data sets of banks and the like.

The invention discloses a human body behavior identification method based on a motion cooperation space. The method comprises the following steps: S1, respectively carrying out key frame extraction based on a motion state measurement coefficient on an initial skeleton sequence and a depth map sequence; s2, extracting motion coordination space vectors from the skeleton sequence processed in the step S1, and splicing the motion coordination space vectors into motion coordination space features; extracting DMM features from the depth map sequence processed in the step S1 to obtain a depth motion map; and S3, inputting the deep motion map and the motion collaborative spatial features into a deep network at the same time, and performing score fusion. Based on the idea of multi-modal fusion, skeleton data and depth data are combined, so that the data are more complete, complementation of various heterogeneous information is realized, redundancy among modals is eliminated, a new behavior recognition method system is established, and a new thought and theoretical basis are provided for research and application of a human body behavior recognition method.

The invention discloses a biological feature multi-modal fusion recognition method and device, a storage medium and equipment, and belongs to the field of biological recognition. Adjusting a comparison threshold value of the latter modal according to the quality score of the biological characteristic image of the latter modal in the multi-modal identification; when the comparison score of the latter mode is not smaller than the adjusted comparison threshold value of the latter mode and the quality score of the biological feature image of the latter mode is not smaller than the decision threshold value of the latter mode, judging that the recognition is passed or performing the recognition of the subsequent modes; otherwise, fusing the comparison scores of all the identified modals to obtain a fused score; and according to the fusion score, judging whether the identification is passed or carrying out subsequent modal identification. According to the method, the comparison threshold value is dynamically adjusted according to the quality score of the biological feature image, auxiliary decision making is carried out on the recognition result through the quality score of the biological feature image, meanwhile, the probability of identity authentication is gradually increased in combination with comparison score fusion, and the safety and reliability of biological identity recognition are improved.

The invention discloses a biometric feature recognition multi-modal fusion method and device, a computer readable storage medium and equipment, and belongs to the field of biometric recognition. In the process of registration and verification/recognition of biological characteristics, various biological characteristic samples are collected, the mass fraction of the biological characteristic samples are obtained through sample quality judgment, the mass fraction and preset fusion threshold control parameters are compared according to a fusion strategy, and if fusion conditions are met, fusion processing is carried out. The fusion comprises sample data fusion, feature data fusion, comparison score fusion and comparison result fusion. Whether the fusion condition is met or not is judged according to the mass fraction of the multiple biological characteristic samples, fusion of the multiple biological characteristics is achieved, the limitation existing when identity recognition authentication is carried out through a single biological characteristic in a traditional method can be overcome, biological characteristic fusion processing is more reasonable and more effective, and a more comprehensive and safer guarantee is provided for identity identification and authentication.

Various embodiments of the present invention provide systems, methods, and computer-program products for fusing at least two scores. In various embodiments, at least two scores are received in which each score predicts the probability of an outcome associated with a particular unit. In particular embodiments, A mass and a distance are calculated between two objects based on the at least two scores in which the first of the two objects is a constant and the second of the two objects comprises one or more characteristics of the particular unit. Further, in particular embodiments, a gravitational force between the two objects is calculated based on the mass and the distance and this gravitational force is used as a fused score for the at least two scores.