12results about How to "Reduce rejection rate" patented technology

The invention provides an access control system based on face recognition and a recognition method, and the system comprises a multi-modal image collection subsystem which integrates a visible light camera, a depth camera and an infrared thermal imaging camera and is used for synchronously collecting a visible light image, a depth image and an infrared thermal imaging image of a face; the image preprocessing unit is used for carrying out corresponding preprocessing on the image; the feature extraction network module is used for extracting image features; the multi-modal feature fusion and alignment module is used for calculating a mutual information matrix by adopting a feature fusion strategy based on mutual information maximization and adaptive weighting, determining an adaptive weight and performing weighted summation, and performing feature alignment by applying a method based on canonical correlation analysis; and the face recognition classification and verification module verifies the image by adopting a multi-level fusion strategy. According to the method, the false recognition rate and the rejection rate are effectively reduced, the strong anti-cheating capability is achieved, and cheating attacks of various fake face means can be accurately recognized and resisted.

The invention discloses a customized order receiving method and system for a designated driver based on a saas platform. The invention relates to the technical field of designated driving services. Receiving demand data of a driver, wherein the demand data comprises order receiving preference, a service area and service time; receiving personal information input by a driver, wherein the personal information comprises basic information and order receiving preference setting; wherein the demand data comprises order receiving preferences, service areas and service time of drivers and users; wherein the basic information comprises a name, a driving age and a vehicle type; according to the invention, the driver is allowed to set personalized order receiving conditions according to own order receiving preference, service area and time, so that the working flexibility of the driver is greatly improved. The driver can autonomously select the order suitable for himself / herself, so that the rejection rate of improper orders is reduced, and the working efficiency and the satisfaction degree are improved. And through an intelligent order receiving matching algorithm, the system can quickly match the most appropriate order for the driver, so that the empty driving time and waiting time of the driver are reduced, and the working efficiency and income are further improved.

The invention relates to the technical field of identity verification, in particular to an identity verification method and system based on multi-modal data fusion, and the method comprises the following steps: based on user login information, comparing the login information with pre-stored information, recognizing a user identity, and extracting pre-stored face data and iris data of a user to obtain a user identity information extraction result. According to the method, the accuracy of identity verification is improved by combining face and iris recognition technologies, optimizing image quality and comparing pre-stored data, the possibility of wrong recognition and identity fraud can be remarkably reduced, and flexible response can be performed according to different security requirements by dynamically adjusting a matching degree verification threshold value; according to the method, the adaptability and the protection degree of the verification process are enhanced, the false identification rate and the false rejection rate can be effectively reduced, meanwhile, a stricter security verification process is provided, higher-level personal identity protection is realized, identity embezzlement and other related fraudulent behaviors are effectively prevented, and the trust of consumers to digital services is enhanced.

The invention discloses a more efficient lattice signature method based on lattice codes, and relates to the technical field of cryptography and information security, the method is improved on the basis of a Fiat-Shamir lattice signature scheme, a high-order hash technology and a one-dimensional character string are used for replacing a randomness technology of a multi-dimensional vector, and the more efficient lattice signature method is constructed. The rejection condition of the signature process can be optimized, so that the rejection frequency of the scheme is reduced, and meanwhile, the time required for single operation is reduced.

This application provides a training method for a fingerprint recognition network, a fingerprint recognition method, an apparatus, and a medium. The training method for the fingerprint recognition network includes: determining fingerprint feature matching maps based on a first initial fingerprint image and a second initial fingerprint image; forming a training sample set from multiple fingerprint feature matching maps; and training an original neural network based on the training sample set to obtain a fingerprint recognition network. The technical solution provided by this application solves the problem of difficulty in distinguishing whether two different fingerprint images belong to the same finger, thus reducing the false acceptance and false recognition rates of fingerprint recognition.

The invention provides a small-area fingerprint identification method and system and a storage medium, and the method comprises the steps: obtaining a fingerprint image, and inputting the fingerprint image into a pre-trained fingerprint feature extraction network model; a block-by-block reasoning strategy is adopted for the fingerprint image through the fingerprint feature extraction network model to obtain a fingerprint low-dimensional feature map; obtaining a fingerprint high-dimensional feature map based on the low-dimensional feature map through a layer-by-layer reasoning strategy; processing the fingerprint high-dimensional feature map through a preset two-dimensional convolution layer, an average pooling layer and a full connection layer in sequence to obtain a final fingerprint feature vector; and calculating the similarity between the fingerprint image to be identified and the target fingerprint image through a cosine similarity function based on the final fingerprint feature vector, and completing fingerprint identification when a set threshold value is reached. Wherein the fingerprint feature extraction network model is obtained by training a fingerprint feature extraction neural network through a pre-constructed fingerprint identification data set. The problems that existing small-area fingerprint recognition is low in precision and low in speed are solved.

The application provides a parallel guide rail base mounting method, comprising the following steps: first, providing guide rail base segments, each guide rail base segment comprising a bottom support, a horizontal panel and a fixed base plate connected in sequence; then, making a mounting reference line, determining the mounting position of the parallel guide rail base according to the mounting reference line; then, mounting the parallel guide rail base to a predetermined position, and confirming and processing the position where the mounting allowance of the bottom support is out of tolerance; finally, butting and mounting the parallel guide rail base with the hull plate of the corresponding mounting position, and repairing and replacing the fixed base plate with mounting precision out of tolerance. By controlling the mounting precision of the guide rail base segment, the mounting precision of the parallel guide rail base, and then adjusting the fixed base plate, the parallelism and flatness mounting requirements of the parallel guide rail base can be met, the mounting precision is ensured, the on-site installation difficulty and waste rate are reduced, and the use cost of personnel and materials is greatly saved.

The invention relates to a cloud resource intelligent performance evaluation system and method based on a large model, and belongs to the technical field of IT resource management. The system comprises a front-end interaction layer, a knowledge management layer, an AI evaluation layer and a data support layer. The method comprises the following steps: in an application stage, guiding a user to clarify a demand and extract key parameters through multiple rounds of dialogues, automatically matching a calculation formula template and generating a recommendation configuration report; in the review stage, the application document is automatically analyzed, normalization, accuracy and logic compliance verification is carried out based on the knowledge base, and review suggestions are generated. According to the invention, through fusion of the large model, the RAG and other technologies, intelligence and standardization of the resource application and review process are realized, the process efficiency and decision consistency are significantly improved, the operation cost and dependence on expert experience are reduced, and structured precipitation and reuse of enterprise knowledge are realized.

The invention relates to the technical field of identity recognition, in particular to an identity recognition system based on finger veins and finger creases, and the system comprises a finger vein recognition model which is used for extracting and analyzing finger vein features, a channel importance evaluation method, and distinguishing important channels and unimportant channels in feature mapping by calculating the importance of each channel; according to the two-level fusion method based on the finger creases and the finger veins, the two features of the finger veins and the finger creases are fused from the feature level and the decision-making level for identity recognition, the two biological features of the finger veins and the finger creases are combined, and the identity recognition accuracy can be remarkably improved by means of the uniqueness of the two biological features of the finger veins and the finger creases. The finger vein feature is located in the finger and is difficult to copy, and the finger crease feature has high individual difference, so that the false recognition rate and rejection rate can be greatly reduced by combining the finger vein feature and the finger crease feature, and the safety and reliability of identity recognition are improved.

The invention discloses a risk prevention and control method and device for a financial manual seat and electronic equipment, and relates to the field of financial science and technology or other related technical fields, and the method comprises the steps: collecting a real-time voice signal interacted between the financial manual seat and a customer, and extracting a multi-dimensional dynamic feature from the real-time voice signal, the multi-dimensional dynamic features comprise acoustic features, semantic features and behavior features; matching the multi-dimensional dynamic features with a pre-constructed customer voiceprint model; based on the matching result and the multi-dimensional dynamic characteristics, evaluating the risk level of the real-time voice signal by adopting a risk evaluation algorithm; and under the condition that the risk level is greater than a preset risk threshold value, sending a risk early warning signal to a financial manual seat, and providing prevention and control suggestions and measures. According to the invention, the technical problem of low accuracy of a voiceprint recognition strategy adopted when a financial institution carries out telephone banking service security verification in the prior art is solved.

The invention discloses an adaptability adjusting and verifying device for a double-channel cut tobacco suction guide rail of a cigarette making machine, the double-channel cut tobacco suction guide rail comprises three guide rail bodies, a channel between adjacent guide rail bodies forms a cut tobacco suction channel, each cut tobacco suction channel is provided with a chopper position, a chopper device is arranged below the chopper position of each cut tobacco suction channel, and the chopper device is connected with the guide rail bodies. The opposite vertex parts of two chopper discs of the chopper device are trimming parts, the verification device comprises a verification guide rail, the verification guide rail is matched with the chopper position section of the yarn suction channel, the verification guide rail is detachably connected to the top of the yarn suction channel in a hanging mode through a hanging structure, and the top face of the verification guide rail is attached to the bottom of a ceramic bead at the chopper position of the yarn suction channel. The bottom of the verification guide rail adsorbs adjusting sheets through magnetic attraction force, and adjustment of different verification heights of the verification device is realized by increasing or decreasing the adjusting sheets. The device has the advantages that the adjusting height position of the chopper disc can be quickly and accurately checked, and the trimming requirements of produced cigarettes of different specifications can be met.

This application discloses a method, apparatus, device, and storage medium for identifying biological objects. The method includes: identifying a target biological object using a biometric model and determining the identification result. The biometric model is established by using biological sample images as training samples and training the model under a class balance loss function. The class balance loss function includes a first additive term and a second additive term. The first additive term is used to balance the training loss corresponding to the first additive term during model training, and the second additive term is used to balance the training loss corresponding to the second additive term during model training. It is evident that the introduction of a class balance loss function during model training reduces the loss of classes with a larger number of classes and increases the loss of classes with a smaller number of classes, thus relatively balancing the contribution of different classes to the loss. This mitigates the impact of long-tailed data on model performance and reduces the false positive and false negative rates.