46 results about "Biometric system" patented technology

Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and / or continuously, in some cases in addition to actively and / or statically, enrolling speakers as the speakers speak into or around an edge device (e.g., car, television, radio, phone). The system identifies users on the fly without requiring a new speaker to mirror prompted utterances for reconfiguring operations. The system manages speaker profiles as speakers provide utterances to the system. Machine-learning architectures implement a passive and continuous voice biometrics system, possibly without knowledge of speaker identities. The system creates identities in an unsupervised manner, sometimes passively enrolling and recognizing known or unknown speakers. The system offers personalization and security across a wide range of applications, including media content for over-the-top services and IoT devices (e.g., personal assistants, vehicles), and call centers.

Systems and methods are disclosed for dual, simultaneous, single-session, proximity-based, secure authentication of a Digital Assisted Shopping (DAS) representative and a customer in an unsecured remote location. The method includes installing a mobile banking application on the customer's device and an enterprise application on the DAS representative's device, both with biometric verification. Proximity detection using Bluetooth Low Energy (BLE) initiates a secure session via push notifications. A secure communication channel is established through a secure local handshake, involving encryption key exchange and mutual authentication. The system exchanges data related to customer profiles and financial accounts, continuously monitors geolocation using GPS, Wi-Fi, and cellular data, and performs periodic background biometric re-verifications. AI / ML algorithms analyze customer data to propose financial products and services, which are securely shared with the customer for review and selection. The system facilitates real-time enrollment and transaction processing, terminating the session upon detecting security breaches.

An enrollment assistance device comprising a carrier with a first surface portion for a user device, and a second surface portion for a contactless smartcard; a first wireless power transfer arrangement supported by the carrier and arranged in the first surface portion of the carrier; a second wireless power transfer arrangement supported by the carrier and arranged in the second surface portion of the carrier; and a conductor arrangement supported by the carrier and conductively connecting the first wireless power transfer arrangement and the second wireless power transfer arrangement.

In a biometric identification system, a transparent body can have a curved platen surface. The curved platen surface can be removably contacted by an anatomy of a user. The anatomy can have an anatomical pattern. An illumination source can illuminate the curved platen surface with an illumination beam. The illumination beam can propagate away from the curved platen surface as a return beam having a beam pattern shaped to correspond to the anatomical pattern. An imaging lens can focus the return beam to form a real image of the beam pattern at an image location. Field-curvature-correcting sensor optics can include a sensor. The sensor can sense the real image of the beam pattern at the image location. The field-curvature sensor optics can include a planar sensor and an image-flattener lens. The field-curvature sensor optics can include a curved, convex sensor.

An improved context-aware security system may include an artificial intelligence (Al) reasoning security system. The context-aware security system may be configured to receive security input data from a plurality of input sources, generate a contextual security analysis, identify a security response, and send instructions to a security system output device to execute the security response. The context-aware security system may include a plurality of security input sources configured to provide security inputs and contextual data, including credential readers, physical access controllers, security sensors, environmental sensors, or external information sources. The context-aware security system may include a directive input subsystem configured to receive security directives from an authorized user interface. A plurality of security system output devices may be configured to execute the security response, including credential reading systems, biometric systems, alerting systems, logging systems, or notification systems.

The present disclosure provides a biometric scanning system for generating an image of a user's vasculature. The biometric scanning system comprises a wrist-worn scanning device including NIR sensors for diffuse optical tomography, NIR LEDs, and an NIR camera configured to receive light emitted by the NIR LEDs after having passed through the user. The system processes vascular data collected by the NIR camera and / or the NIR sensors to generate a key corresponding to the user. In conjunction with access control systems, the biometric scanning system facilitates reliable user authentication without storing a root template of that user's vasculature.

In various embodiments, enhancements for low-touch biometric systems include obtaining winnowing information when a biometric probe unsuccessfully matches against a subset gallery. The winnowing information is used to obtain biometric data from an associated group of people and the biometric probe is then compared against the biometric data. If this also fails, identity information may be obtained and used to obtain a corresponding biometric template to match the biometric probe against. In some embodiments, enhancements for low-touch biometric systems determining a dominant by selecting the largest live face in an image then performing one or more actions using the dominant. Such one or more actions may include selecting the dominant for capture, performing exposure control and / or gain control on the dominant as opposed to the rest of an image, determining guidance to provide for biometric capture based at least on a position of the dominant, providing such guidance, etc.

User enrollment to a biometric identification system begins with a pre-enrollment process on selected general input devices (GID) such as smartphones. The user may enter identification data such as their name and use a camera of the GID to acquire first image data, such as of their hand. The first image data is processed to determine a first representation. Upon presentation of a hand at a biometric input device, second image data is acquired. The second image data is processed to determine a second representation. If the second representation is deemed to be associated with the first representation, the enrollment process may be completed by storing the second representation for subsequent use.

User enrollment in a biometric identification system begins with a pre-enrollment process on a selected generic input device (GID), such as a smartphone. The user enters identifying data, such as their name, and may use the GID's camera to capture first image data, such as of their hand. The first image data is processed to determine a first representation. Upon presenting the hand at the biometric input device, second image data is captured. The second image data is processed to determine a second representation. If the second representation is deemed associated with the first representation, the enrollment process may be completed by saving the second representation for later use.

In a biometric identification system, a transparent body can have a curved platen surface. The curved platen surface can be removably contacted by an anatomy of a user. The anatomy can have an anatomical pattern. An illumination source can illuminate the curved platen surface with an illumination beam. The illumination beam can propagate away from the curved platen surface as a return beam having a beam pattern shaped to correspond to the anatomical pattern. An imaging lens can focus the return beam to form a real image of the beam pattern at an image location. Field-curvature-correcting sensor optics can include a sensor. The sensor can sense the real image of the beam pattern at the image location. The field-curvature sensor optics can include a planar sensor and an image-flattener lens. The field-curvature sensor optics can include a curved, convex sensor.

Systems and methods for a projected dielectric sensor comprising a first conductive path with a first conductive mesh, a second conductive path with a second conductive mesh, and a dielectric gap between them. The dielectric gap functions as an open circuit in a resting state, offering a broader detection range compared to traditional sensors. The sensor includes a power drive connected to the first conductive path and a collector connected to the second conductive path. The first and second conductive meshes include sheets that are positioned longitudinally side-by- side, enhancing sensitivity and simplifying manufacturing. In an example, the sheets are separated by at least 10 cm of dielectric material. The sensor can detect off-axis objects such as conductors, vehicles, humans, and baby seats, making it suitable for various applications including vehicle safety, biometric systems, gesture and mid-air control, and machine learning integration.

Provided herein are systems and methods for authenticating a customer for a completing different types of restricted transactions. More specifically, provided herein are biometric authentication systems and processes for completing transactions where the goods and services at issue are subject to one or more transaction conditions, such as age-related restrictions. The systems described herein involve a point-of-interaction device that facilitates biometric authentication by obtaining and verifying an individual's biometric information, such as fingerprints, palm prints, or facial scans. This biometric data is then used to determine whether the individual meets the necessary conditions for the restricted transaction, such as age verification. The system aims to enhance security and efficiency in transactions by eliminating the need for physical cards and reducing the risk of fraud.

The technologies discussed herein relate to a biometrics system comprising one or more processors, computer storage memory having computer-executable instructions, surgically implantable devices, and wearables (e.g., an apparel item configured for an upper torso, an apparel item configured for a lower torso, a sock, a headband, other types of wearables, or one or more combinations thereof) having one or more biometric sensors (e.g., a heart rate sensor, a sweat sensor, a glucose sensor, other types of biometric sensors, or one or more combinations thereof) and one or more sensation devices (e.g., a plurality of visible light sources, a haptic feedback device, other types of sensation devices, or one or more combinations thereof). The wearables may also include one or more environmental sensors. The wearables emulate, transmogrify, or enhance one or more human senses using the one or more sensation devices based on biometric data or environmental data received.

A biometric identification system uses inputs of a user that are acquired using different modalities. A model having an intersection branch is trained to determine an embedding using features present in two or more modalities (an intersection of modalities). A first input acquired using a first modality is processed by the model to determine a first embedding. A second input acquired using a second modality is also processed by the model to determine a second embedding. Comparison of the first embedding and the second embedding may be used to determine if the first input and the second input are associated with the same user.

A nanocomposite proximity-pressure sensor (NPPS), configured to measure one or more human biometrics, the NPPS including a carbon nanotube-paper (CPC) electrode, a multiwalled carbon nanotubes (MWCNTs) foam, and an auxetic MWCNTs-embedded frame, wherein the auxetic MWCNTs-embedded frame is positioned around the MWCNTs foam, wherein when the auxetic frame is compressed, it expands and exerts a pressure onto the MWCNTs foam.

The present disclosure provides techniques and apparatus for capturing an image of a person's retina fundus, identifying the person, accessing various electronic records (including health records) or accounts or devices associated with the person, determining the person's predisposition to certain diseases, and / or diagnosing health issues of the person. Some embodiments provide imaging apparatus having one or more imaging devices for capturing one or more images of a person's eye(s). Imaging apparatus described herein may include electronics for analyzing and / or exchanging captured image and / or health data with other devices. In accordance with various embodiments, imaging apparatus described herein may be alternatively or additionally configured for biometric identification and / or health status determination techniques, as described herein.

A method is provided for determining correctives to statistical identification variances of a biometric identification system using facial and / or pedestrian recognition. The method includes (a) defining a set of categories, each category having at least one corresponding physical appearance characteristic; (b) extracting, for each individual, at least one physical appearance characteristic from at least one image of the individual; (c) assigning, to each individual, at least one category on the basis of the extracted physical appearance characteristic; (d) distributing, for each category, each individual into two groups according to the failure or success of their identification by the system; and (e) calculating, for each category, a number of individuals to be selected in the group corresponding to the success of the identification such that the relative proportions of individuals in the group corresponding to the failure of the identification are substantially equal between all the criteria relating to said category.

Provided is a method for securing a biometric recognition of a user to be recognized against one or more templates and a non zero integer, corresponding to registered users identities, using a biometric sample of said user, in a biometric recognition system comprising a plurality of computation devices, a global scoring computation device and a recognition device. Each of said templates is split, using an additive splitting modulo a predetermined integer N, into a plurality of template shares stored in said computation devices, with i in [0, n−1] and n a non zero integer. A global scoring is computed for said at least one template by combining said shares of the global scoring computed for said template by said plurality of computation devices according to a formula. Other embodiments disclosed.

A biometric identification system processes input data acquired by input devices to determine embeddings used to identify a user. Different types of input devices or hardware configurations of input devices may produce different output. Each hardware configuration may be associated with respective representation data. A set of transformer networks are used to transform an embedding from one representation data associated with a first type of device or hardware configuration to another. This enables user participation via different configurations of hardware without requiring users to re-enroll for different input devices or hardware configurations. Opportunistic updates are made to the embeddings as embeddings native to a particular configuration of hardware are acquired from the user.

This application provides a biometric system for complex scenarios, relating to the field of biometric technology. By setting up multiple cameras that can be deployed in any area of ​​the monitoring site, the system allows for flexible adjustment of the camera's installation position and number based on the actual layout of the complex scenario and the key monitoring areas. This achieves full coverage of the monitoring site without blind spots, reducing reliance on manual inspections and solving the monitoring challenges of multiple and irregular areas in complex scenarios. It is suitable for rodent monitoring needs in various complex scenarios such as warehousing, agriculture, and urban public facilities. By accessing the video streams from multiple cameras through an edge computing device and running an AI visual analysis model locally, the system enables real-time local analysis of the video streams and identification of specific organisms (mice). This eliminates the need to upload all video streams to the cloud, reducing network bandwidth consumption and mitigating transmission latency issues caused by centralized cloud processing.

The invention discloses a multi-modal biological recognition fusion system, which comprises a multi-modal data acquisition engine, a dynamic cross validation engine and an identity label generation module, and the multi-modal data acquisition engine, the dynamic cross validation engine and the identity label generation module are connected with the dynamic cross validation engine and the identity label generation module. The feature level fusion module is used for realizing dynamic weight distribution and feature level fusion by integrating multi-dimensional biological features such as fingerprints, human faces, irises and voiceprints, and constructing a unique identity binding architecture in a high-security scene; according to the method, the technical bottlenecks of a traditional biological recognition system in the aspects of security boundary, environmental adaptability and anti-attack ability can be broken through by constructing a multi-modal feature dynamic fusion framework and creatively integrating the generative adversarial network, federated learning and edge computing optimization technologies.

This utility model relates to microprocessor technology, biometric identification systems, and embedded intelligent devices designed for automatic personal recognition based on multiple biometric characteristics. The proposed utility model is a multimodal microprocessor-based biometric identification device comprising a fingerprint scanner, a voice identification module, a microprocessor computing module, a memory module, a neural network data processing unit, a decision-making unit, a communication interface, and a power source. A distinctive feature of the utility model is its use of a unified microprocessor architecture, enabling local intelligent processing of multiple biometric modalities without the need to transmit data to external computing servers.The microprocessor computing module collects, digitally filters, extracts features, analyzes, and compares user biometric data with templates stored in the device's memory. Fingerprints and voice characteristics are used as biometric features. Combined processing of multiple biometric parameters improves identification accuracy, reduces the likelihood of false positives and false negatives, and enhances the system's resilience to external influences and noise. The technical result of this utility model is increased reliability, speed, and autonomy of biometric identification through the use of integrated microprocessor processing of multimodal biometric data and recognition algorithms.The utility model can be used in access control and management systems, banking and financial systems, mobile devices, medical information systems, critical infrastructure facilities, government personal identification systems, and intelligent IoT systems. The proposed device is compact, capable of autonomous operation, offers high data processing speeds, and can be integrated into modern digital security systems.

A system and method for projecting a dielectric sensor including a first conductive path with a first conductive mesh, a second conductive path with a second conductive mesh, and a dielectric gap therebetween. The dielectric gap acts as an open circuit in a resting state, providing a wider detection range compared to traditional sensors. The sensor includes a power driver connected to the first conductive path and a collector connected to the second conductive path. The first and second conductive meshes include sheets positioned longitudinally side-by-side, improving sensitivity and simplifying manufacturing. In one example, the sheets are spaced apart by at least 10 centimeters of dielectric material. The sensor can detect off-axis objects, such as conductors, vehicles, humans, and baby seats, making it suitable for a variety of applications, including vehicle safety, biometric systems, hand gestures and air control, and machine learning integration.

Embodiments described herein provide for a voice biometrics system execute machine-learning architectures capable of passive, active, continuous, or static operations, or a combination thereof. Systems passively and / or continuously, in some cases in addition to actively and / or statically, enrolling speakers as the speakers speak into or around an edge device (e.g., car, television, radio, phone). The system identifies users on the fly without requiring a new speaker to mirror prompted utterances for reconfiguring operations. The system manages speaker profiles as speakers provide utterances to the system. Machine-learning architectures implement a passive and continuous voice biometrics system, possibly without knowledge of speaker identities. The system creates identities in an unsupervised manner, sometimes passively enrolling and recognizing known or unknown speakers. The system offers personalization and security across a wide range of applications, including media content for over-the-top services and IoT devices (e.g., personal assistants, vehicles), and call centers.

The disclosed invention introduces a novel fingerprint feature matching method that employs a graph neural network to calculate a similarity score between two sets of fingerprint features. By learning distinctive feature representations and graph aggregations in an end-to-end manner, the method eliminates the need for manual feature descriptor construction and manual matching formulas. The method is not limited to fingerprints, as it can utilize various local fingerprint features such as feature points, pores, and singularities, and can also work with other friction ridge patterns such as palm prints and footprints. The method can be integrated into biometric systems to reliably identify individuals based on fingerprints or other friction ridge patterns.

An enrollment assistance device comprising a carrier with a first surface portion for a user device, and a second surface portion for a contactless smartcard; a conductor spiral supported by the carrier and overlapping the first surface portion and the second surface portion of the carrier. The conductor spiral is configured to form a first wireless power transfer link with the wireless power transfer arrangement of the user device when the user device is arranged on the first surface portion of the carrier, and a second wireless power transfer link with the wireless power transfer arrangement of the contactless smartcard when the contactless smartcard is arranged on the second surface portion of the carrier.

An enrollment assistance device with a first surface portion for a user device, and a second surface portion for a contactless smartcard; a first conductor spiral arranged in the first surface portion; a second conductor spiral conductively separated from the first conductor spiral and arranged in the second surface portion. The first conductor spiral and the second conductor spiral are coupled to each other by capacitive coupling pads.

A vehicle biometric system includes a processor and a non-transitory, processor-readable storage medium communicatively coupled to the processor, the non-transitory, processor-readable storage medium including one or more instructions stored thereon that, when executed, cause the processor to obtain benchmark biometric data from a first data source; obtain in-vehicle biometric data from a second data source; transmit the benchmark biometric data from the first data source and the in-vehicle biometric data from the second source to a neural network; update a model based on training data by processing the benchmark biometric data and the in-vehicle biometric data; transmit the updated model; ascertain at least one of an occupant condition and an emotional state of an occupant based on the updated model; and control one or more vehicle action events based on ascertaining at least one of the occupant condition and the emotional state of the occupant.

A non-invasive biometric system includes a processor configured to control a scanner configured to scan and capture one or more anatomical images of a target person's body. The processor is also configured to identify one or more anatomical structures in the captured anatomical images and extract anatomical features from the identified anatomical structures. The processor is further configured to register the extracted anatomical features of the identified anatomical structures to the target person's posture and appearance. The processor is also configured to encode and utilize the extracted anatomical features as biometric data unique to the target person and usable for authentication of the target person.

A system and method for projecting a dielectric sensor that includes a first conductive path with a first conductive web, a second conductive path with a second conductive web, and a dielectric gap therebetween. The dielectric gap serves as an open circuit in a stationary state, providing a wider detection range compared to conventional sensors. The sensor includes a power driver connected to the first conductive path and a collector connected to the second conductive path. The first and second conductive webs include sheets positioned longitudinally side-by-side, thereby improving sensitivity and simplifying manufacturing. In one example, the sheets are spaced apart by a dielectric material of at least 10 centimeters. The sensor may detect off-axis objects, such as conductors, vehicles, humans, and baby seats, making them suitable for use in a variety of applications, including vehicle safety, biometric systems, gestures and air control, and machine learning integration.