A processing system based on facial feature recognition

Abstract

This invention discloses a facial feature recognition-based processing system, comprising modules for image acquisition, feature extraction, recognition and comparison, data storage and management, and result output and interaction. Image acquisition utilizes multi-source fusion technology, feature extraction employs a quantum neural computing model, recognition and comparison leverages a chaotic encryption algorithm, data storage combines blockchain and intelligent compression technology, and result output supports various innovative methods. These modules work collaboratively to achieve efficient and accurate facial feature recognition, meeting the needs of different scenarios. The system also includes corresponding innovative methods and related technologies applied to each step to ensure efficient system operation. This facial feature recognition system has several advantages: it can acquire high-quality images in complex environments, accurately extract features, perform rapid and secure comparison, reliably store data, and provide innovative output methods to meet the needs of different scenarios, improving recognition accuracy, security, and user experience.

Description

Technical Field

[0001] This invention relates to the field of facial recognition system technology, and more specifically, to a processing system based on facial feature recognition. Background Technology

[0002] With the rapid development of information technology and the increasing demands for social security, facial recognition technology is playing an increasingly important role in many fields. However, existing facial recognition systems still face numerous challenges and difficulties.

[0003] In image acquisition, traditional systems are often severely limited by environmental factors. For example, in low-light conditions, especially at night or in dimly lit indoor environments, the quality of acquired facial images deteriorates significantly, resulting in blurriness and excessive noise, which greatly increases the difficulty of subsequent feature extraction and recognition. Moreover, in scenarios with complex background interference, such as crowded scenes or scenes with drastic changes in background lighting, it is difficult to accurately focus on and separate facial images, affecting the accuracy and efficiency of acquisition.

[0004] In the feature extraction stage, existing technologies fall short in terms of the comprehensiveness and accuracy of facial feature extraction. They struggle to effectively extract and analyze subtle facial features, such as minute changes in skin texture and subtle differences in facial expressions. Furthermore, their ability to distinguish similar features between different individuals is limited, leading to misidentification or low accuracy. Moreover, traditional feature extraction algorithms have high computational complexity, resulting in slow processing speeds and making them unsuitable for applications with high real-time requirements.

[0005] During the identification and comparison process, existing systems have shown significant limitations when faced with large-scale data and complex and ever-changing facial features. The efficiency and accuracy of the matching algorithms need improvement, especially when processing massive facial data databases, where the search and comparison times are too long and cannot meet the requirements for rapid response. Moreover, they are poorly adaptable to dynamic changes in faces, such as aging, changes in makeup, and changes in hairstyle, which can easily lead to recognition failures or misidentifications.

[0006] Traditional systems suffer from security and reliability vulnerabilities in data storage and management. Data storage is susceptible to hacking and data tampering, potentially leading to the leakage of facial recognition data and posing serious risks to user privacy and security. Furthermore, traditional storage methods are costly and lack scalability for storing and managing large-scale data, making it difficult to meet the ever-increasing demands for data storage.

[0007] In terms of output and interaction, existing systems are often simplistic, lacking flexibility and innovation. They can only provide simple text or image displays, failing to meet the diverse needs of users and the requirements of complex application scenarios. For example, in scenarios requiring real-time interaction and immersive experiences, such as virtual reality and augmented reality applications, existing systems cannot provide effective support. Summary of the Invention

[0008] The present invention proposes a processing system based on facial feature recognition to solve the problems mentioned in the prior art.

[0009] To achieve the above objectives, the present invention adopts the following technical solution: a processing system based on facial feature recognition, comprising an image acquisition module, a feature extraction module, a recognition and comparison module, a data storage and management module, and a result output and interaction module.

[0010] The image acquisition module employs multi-source fusion imaging technology, combining a high-resolution optical sensor and a depth sensor, enabling it to accurately capture the 3D shape and rich texture information of faces in various complex environments. For example, infrared-assisted imaging is used in low-light environments to ensure clear acquisition of facial images. Its image acquisition quality evaluation formula is as follows: Q image R represents the image acquisition quality. resolution D represents the image resolution. depth_accuracy S represents the accuracy of depth data. stability F represents image stability. adaptability N represents environmental adaptability. noise This indicates image noise.

[0011] The feature extraction module employs a quantum neural computing model. Based on the superposition and entanglement properties of qubits, this model can efficiently extract the fusion information of microscopic and macroscopic facial features, including but not limited to the fine structural features of facial features, dynamic changes in facial muscles, and unique bioelectrical signal characteristics. The formula for evaluating the accuracy of feature extraction is as follows: Where A extract N represents the accuracy of feature extraction. rorrect_features N represents the number of correctly extracted features. total_features E represents the total number of features to be extracted. quantum_enhancement (This represents the quantum computing enhancement factor).

[0012] The identification and comparison module employs a high-speed matching algorithm based on chaotic encryption to quickly and securely compare extracted facial features with features in the database. It leverages the randomness and complexity of chaotic systems for encrypted data transmission and dynamic key generation during the matching process, ensuring data security and identification accuracy. Its identification and comparison speed evaluation formula is as follows: Where V recognition N represents the recognition and comparison speed. faces_processed T represents the number of faces processed per unit of time. processing Indicates processing time, C chaos_factor This represents the acceleration factor for chaotic encryption.

[0013] The data storage and management module combines a blockchain distributed storage architecture with intelligent data compression technology. Blockchain ensures data immutability and traceability, while intelligent data compression technology reduces storage costs and improves storage efficiency while maintaining data integrity. Its data storage capacity evaluation formula is as follows: Where C storage N represents the data storage capacity. compressed_data_stored S represents the actual amount of compressed data stored. storage_space D represents the total space of the storage device. blockchain_efficiency This represents the blockchain storage efficiency factor.

[0014] The output and interaction module supports a variety of innovative output methods, including but not limited to virtual reality (VR) immersive feedback, augmented reality (AR) visual guidance, and brain-computer interface (BCI) thought-based interactive responses. For example, in the security field, AR technology can be used to overlay recognition results onto real-world scenes as virtual tags for security personnel, or BCI devices can allow users to interact with the system through thought commands.

[0015] Preferably, the multi-source fusion imaging technology in the image acquisition module supports automatic environmental perception and adaptive parameter adjustment, and can automatically optimize imaging parameters according to different environmental factors such as light, temperature, and humidity.

[0016] Preferably, the quantum neural computing model in the feature extraction module is optimized and trained using the quantum annealing algorithm to improve the performance and efficiency of feature extraction.

[0017] Preferably, the high-speed matching algorithm based on chaotic encryption in the identification and comparison module supports dynamic key updates and multimodal feature fusion comparison to improve the accuracy and security of identification.

[0018] Preferably, the blockchain distributed storage architecture in the data storage and management module uses smart contracts to achieve automated management of data access permissions and secure control of data sharing.

[0019] Preferably, the various innovative output methods in the result output and interaction module support user personalization and scene adaptive switching to meet the needs of different users and application scenarios.

[0020] A facial feature recognition-based processing method includes the following steps:

[0021] Image acquisition: The image acquisition module uses multi-source fusion imaging technology and automatic environmental perception technology to acquire image information containing human faces, and performs preliminary image preprocessing, such as noise reduction and contrast enhancement.

[0022] Feature extraction: The preprocessed image is transmitted to the feature extraction module, where quantum neural computing model and quantum annealing algorithm are used to efficiently extract facial features.

[0023] Recognition and comparison: The extracted facial features are securely and quickly compared with features in the database using a high-speed matching algorithm based on chaotic encryption.

[0024] Data storage: The recognition results and facial feature data are securely stored using a blockchain distributed storage architecture and intelligent data compression technology.

[0025] Results Output: The results output and interaction module presents the recognition results to users or relevant systems in a variety of innovative output methods and supports user interaction.

[0026] Preferably, in the image acquisition step, dynamic tracking technology is used to ensure that the face is always in the optimal imaging position.

[0027] Preferably, in the feature extraction step, feature enhancement techniques are used to highlight and optimize key features.

[0028] Preferably, in the result output step, emotion perception technology is used to adjust the output method and content according to the user's emotional state and attention focus.

[0029] Compared with existing technologies, the beneficial effects of this invention are:

[0030] 1. Superior image acquisition capabilities:

[0031] By employing multi-source fusion imaging technology and automatic environmental perception, high-quality facial images can be acquired in various complex environments. Whether in low light, strong light, or complex backgrounds, clear and accurate facial images are ensured, providing a solid foundation for subsequent processing. For example, in nighttime security monitoring, facial features can be clearly captured, significantly improving the effectiveness of surveillance.

[0032] 2. Efficient and accurate feature extraction:

[0033] By employing quantum neural computing models, it is possible to comprehensively and accurately extract various facial features, including the fusion of microscopic and macroscopic features. This not only precisely identifies the fine structure of facial features but also captures dynamic changes in facial muscles and unique bioelectrical signal characteristics. This significantly improves feature recognition and distinguishability, reduces the probability of false identification, and substantially enhances recognition accuracy. For example, in identity authentication scenarios, it can accurately distinguish between individuals with similar faces, ensuring the security and reliability of authentication.

[0034] 3. Fast and secure identification and comparison:

[0035] Employing a high-speed matching algorithm based on chaotic encryption ensures both rapid identification and comparison while enhancing data security. When faced with large-scale facial data, it enables rapid searching and comparison, significantly reducing response time and meeting the demands of real-time applications. Simultaneously, chaotic encryption technology ensures data security during transmission and comparison, preventing data theft or tampering and protecting user privacy and data security. For example, in facial recognition payment within the financial sector, it enables rapid payment verification while safeguarding user funds.

[0036] 4. Reliable data storage and management:

[0037] By combining blockchain's distributed storage architecture with intelligent data compression technology, secure data storage and efficient management are achieved. Blockchain technology ensures data immutability and traceability, improving data security and trustworthiness. Intelligent data compression technology reduces storage costs and improves storage efficiency while maintaining data integrity, and also facilitates data backup and recovery. For example, in an enterprise's personnel management system, it can securely store large amounts of employee facial feature data and facilitate data management and retrieval.

[0038] 5. Innovative output and interaction methods:

[0039] It supports various innovative output methods, such as virtual reality immersive feedback, augmented reality visualization guidance, and brain-computer interface thought-based interactive responses. This provides users with a richer, more intuitive, and convenient interactive experience, meeting the needs of different users and application scenarios. For example, in the education and training field, virtual reality technology can provide students with an immersive learning experience, and facial recognition technology can achieve personalized teaching interaction; in the medical field, augmented reality technology can provide doctors with visualized information about patients' facial features to assist in medical diagnosis and treatment. Attached Figure Description

[0040] Figure 1This is a schematic block diagram of a face feature recognition-based processing system proposed in this invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0043] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0044] Reference Figure 1 A facial feature recognition-based processing system includes an image acquisition module, a feature extraction module, a recognition and comparison module, a data storage and management module, and a result output and interaction module.

[0045] The image acquisition module employs multi-source fusion imaging technology, combining a high-resolution optical sensor and a depth sensor, enabling it to accurately capture the 3D shape and rich texture information of faces in various complex environments. For example, infrared-assisted imaging is used in low-light environments to ensure clear acquisition of facial images. Its image acquisition quality evaluation formula is as follows: Q image R represents the image acquisition quality. resolution D represents the image resolution. depth_accuracy S represents the accuracy of depth data. stability F represents image stability. adaptability N represents environmental adaptability. noise This indicates image noise.

[0046] The feature extraction module employs a quantum neural computing model. Based on the superposition and entanglement properties of qubits, this model can efficiently extract the fusion information of microscopic and macroscopic facial features, including but not limited to the fine structural features of facial features, dynamic changes in facial muscles, and unique bioelectrical signal characteristics. The formula for evaluating the accuracy of feature extraction is as follows: Where A extract N represents the accuracy of feature extraction. corrext_features N represents the number of correctly extracted features. total_features E represents the total number of features to be extracted. quantum_enhancement (This represents the quantum computing enhancement factor).

[0047] The identification and comparison module employs a high-speed matching algorithm based on chaotic encryption to quickly and securely compare extracted facial features with features in the database. It leverages the randomness and complexity of chaotic systems for encrypted data transmission and dynamic key generation during the matching process, ensuring data security and identification accuracy. Its identification and comparison speed evaluation formula is as follows: Where V recognition N represents the recognition and comparison speed. faces_processed T represents the number of faces processed per unit of time. processing Indicates processing time, C chaos_factor This represents the acceleration factor for chaotic encryption.

[0048] The data storage and management module combines a blockchain distributed storage architecture with intelligent data compression technology. Blockchain ensures data immutability and traceability, while intelligent data compression technology reduces storage costs and improves storage efficiency while maintaining data integrity. Its data storage capacity evaluation formula is as follows: Where C storage N represents the data storage capacity. compressed_data_stored S represents the actual amount of compressed data stored. storage_space D represents the total space of the storage device. blockchain_efficiency This represents the blockchain storage efficiency factor.

[0049] The output and interaction module supports a variety of innovative output methods, including but not limited to virtual reality (VR) immersive feedback, augmented reality (AR) visual guidance, and brain-computer interface (BCI) thought-based interactive responses. For example, in the security field, AR technology can be used to overlay recognition results onto real-world scenes as virtual tags for security personnel, or BCI devices can allow users to interact with the system through thought commands.

[0050] In this invention, the multi-source fusion imaging technology in the image acquisition module supports automatic environmental perception and adaptive parameter adjustment, and can automatically optimize imaging parameters according to different environmental factors such as light, temperature, and humidity.

[0051] In this invention, the quantum neural computing model in the feature extraction module is optimized and trained using the quantum annealing algorithm to improve the performance and efficiency of feature extraction.

[0052] In this invention, the high-speed matching algorithm based on chaotic encryption in the identification and comparison module supports dynamic key updates and multimodal feature fusion comparison to improve the accuracy and security of identification.

[0053] In this invention, the blockchain distributed storage architecture in the data storage and management module fully leverages its unique advantages and innovatively employs smart contracts to achieve automated management of data access permissions and secure control of data sharing.

[0054] Regarding the automated management of data access permissions, smart contracts first establish a comprehensive permission system based on preset rules and policies. When new data needs to be stored or existing data needs to be accessed, the smart contract automatically determines the appropriate access permissions based on factors such as the data type, source, and the user roles involved. For example, for facial feature data involving personal privacy, the smart contract decides whether to allow access based on the user's identity information and authorization status. If the user is the data owner, the smart contract automatically grants full access permissions after verifying their identity, including operations such as viewing, modifying, and deleting data. For other unauthorized users, the smart contract strictly restricts their access permissions, or even completely prohibits access.

[0055] Smart contracts also enable dynamic permission adjustments. As time passes and business scenarios change, data access requirements and security levels may evolve. For example, in a company's human resources management system, when an employee leaves, the smart contract automatically revokes that employee's access to the company's internal facial recognition data. Or, when a project ends, related data access permissions will be adjusted according to preset rules. This dynamic permission management mechanism ensures that data is always accessed and used within a secure and reasonable scope.

[0056] In terms of secure data sharing controls, smart contracts play a crucial role. When data sharing is required, smart contracts automatically execute relevant operations according to pre-defined sharing protocols and security rules. For example, when sharing facial feature data between different departments for collaborative work, smart contracts encrypt and verify the data transmission process to ensure that the data is not tampered with or stolen during transmission. Simultaneously, smart contracts record detailed information for each data sharing session, including the time of sharing, participating parties, and the content of the shared data, for subsequent auditing and traceability.

[0057] Smart contracts also enable fine-grained control over data sharing. They can segment and filter data based on different sharing needs and security levels, sharing only the necessary portions. For example, when collaborating with a third party on market research, it might only be necessary to share anonymized facial feature data, such as age distribution and gender ratio statistics, without sharing specific personal identification information and complete facial image data. This fine-grained control satisfies the need for data sharing while maximizing data security and privacy.

[0058] Furthermore, smart contract execution is based on blockchain's distributed ledger technology, which features decentralization, immutability, and high transparency. This means that once a smart contract is deployed on the blockchain, its execution process will not be controlled or influenced by a single node, and all operation records will be publicly recorded on the blockchain, which any participant can view and verify at any time. This further enhances the security and trustworthiness of data storage and management, providing a solid technical guarantee for data access control and sharing security control.

[0059] In this invention, the various innovative output methods in the result output and interaction module support user personalization and scene adaptive switching to meet the needs of different users and application scenarios.

[0060] A facial feature recognition-based processing method includes the following steps:

[0061] Image acquisition: The image acquisition module uses multi-source fusion imaging technology and automatic environmental perception technology to acquire image information containing human faces, and performs preliminary image preprocessing, such as noise reduction and contrast enhancement.

[0062] Feature extraction: The preprocessed image is transmitted to the feature extraction module, where quantum neural computing model and quantum annealing algorithm are used to efficiently extract facial features.

[0063] Recognition and comparison: The extracted facial features are securely and quickly compared with features in the database using a high-speed matching algorithm based on chaotic encryption.

[0064] Data storage: The recognition results and facial feature data are securely stored using a blockchain distributed storage architecture and intelligent data compression technology.

[0065] Results Output: The results output and interaction module presents the recognition results to users or relevant systems in a variety of innovative output methods and supports user interaction.

[0066] In this invention, dynamic tracking technology is used in the image acquisition step to ensure that the face is always in the optimal imaging position.

[0067] In this invention, feature enhancement technology is innovatively applied in the feature extraction step to highlight and optimize key features.

[0068] Feature enhancement technology first involves multi-dimensional analysis of the acquired facial images. By employing advanced image processing algorithms and mathematical models, the system can meticulously dissect and evaluate various parts and features of the face. For example, for the eyes, the system analyzes their shape, size, contour, and the texture and color distribution of the eyeballs. For the nose, it focuses on details such as the height of the bridge, the width of the nostrils, and the shape of the nasal passages. For the mouth, it studies the shape and color of the lips, as well as the visible features of the teeth. Through detailed analysis of these different features, the system can build a comprehensive and accurate initial model of facial features.

[0069] After determining the initial feature model, feature enhancement techniques employ a series of methods to highlight key features. One important method is contrast enhancement. By adjusting the brightness and color contrast of different regions in the image, key features are made more prominent. For example, the outline of a face is made clearer and more discernible by increasing its contrast with the background. For distinctive facial features such as moles, scars, or special textures, their brightness and contrast are enhanced to make them stand out more. This makes it easier for the system to accurately identify and extract these key features during subsequent feature extraction.

[0070] In addition to contrast enhancement, feature enhancement techniques also employ feature magnification and sharpening. For subtle but crucial features, such as wrinkles around the eyes and fine lines around the mouth, image magnification techniques are used to locally enlarge these areas, allowing the system to capture these details more clearly. Simultaneously, sharpening algorithms are used to enhance the edges of these features, making them sharper and clearer. For example, when identifying older users, these subtle facial wrinkles may be important distinguishing features; feature enhancement techniques ensure that these features are not overlooked during feature extraction.

[0071] Furthermore, feature enhancement techniques also incorporate attention mechanisms from deep learning. The system trains an attention model that automatically learns which features are most critical and discriminative for facial recognition. During feature extraction, this attention model automatically concentrates more computational resources and attention on these key features, thereby further improving the accuracy and reliability of key feature extraction. For example, when faced with a group of people with similar faces, the attention model might focus on unique facial differences, such as differences in eyebrow shape or ear contour, improving recognition accuracy by highlighting the extraction and analysis of these key features.

[0072] Meanwhile, feature enhancement technology also has the ability to dynamically adjust. Depending on the application scenario and the actual condition of the face, the system can automatically adjust the parameters and methods of feature enhancement. For example, in security monitoring scenarios, for fast-moving face images, the system will use a faster and more efficient feature enhancement algorithm to ensure accurate extraction of key features in a short time. In high-precision identity authentication scenarios, the system will perform more detailed and comprehensive feature enhancement processing to meet higher requirements for recognition accuracy.

[0073] In this invention, advanced emotion perception technology is fully utilized in the result output step to intelligently adjust the output method and content according to the user's emotional state and attention focus.

[0074] Emotion perception technology primarily relies on a series of multimodal sensors and intelligent analysis algorithms. First, high-precision biosignal sensors, such as skin conductance sensors and heart rate monitoring sensors, collect users' physiological signal data in real time. These physiological signals can reflect changes in the user's emotions; for example, when a user is excited, skin conductance increases, and the heart rate also accelerates accordingly. Simultaneously, advanced computer vision technology is used to analyze the user's facial expressions and eye contact in real time. For instance, when a user exhibits obvious facial expression changes such as smiling or frowning, the system can quickly identify and determine their emotional state.

[0075] After determining the user's emotional state, the system adjusts its output accordingly based on different emotion types. If the system detects a user in a pleasant emotional state, the output interface can use bright, vibrant colors and cheerful sound effects to enhance the user's positive experience. For example, in an entertainment application scenario, when the system recognizes that the user is enjoying an interactive activity and is in a high mood, it can display the recognition result along with some fun animation effects or play some relaxing music to further enhance the user's entertainment experience. Conversely, when the system senses that the user is in a tense or anxious emotional state, the output will be more concise and clear, avoiding excessive and complex information that might distract the user. For example, in a facial recognition verification scenario for financial transactions, if the system detects that the user is showing signs of tension, it may simplify the verification result display interface, highlighting key pass or fail information and reducing unnecessary text descriptions and decorative elements to help the user quickly understand the result and alleviate their tension.

[0076] To determine the user's focus of attention, the system comprehensively utilizes eye-tracking technology and scene analysis algorithms. Eye-tracking technology accurately monitors the user's eye movement trajectory and gaze point location. For example, when a user is viewing facial recognition results, the system can use eye-tracking data to understand the key areas the user is focusing on on the interface. If the user's attention is primarily concentrated on a specific area of ​​detailed information, such as the identity information section in the recognition results, the system can automatically enlarge the display area or provide more relevant detailed information links. Simultaneously, scene analysis algorithms combine the current application scenario and surrounding environment information to further assist in determining the user's focus of attention. For example, in a smart shopping scenario, if the system detects that a user is standing in front of a product display area undergoing facial recognition, and their gaze is primarily focused near the product's price tag, the system can automatically push promotional information and related purchase suggestions for that product while outputting the facial recognition results.

[0077] Furthermore, the system possesses dynamic adjustment capabilities. As the user's emotional state and focus of attention change, the output method and content are optimized and updated in real time. For example, during a prolonged human-computer interaction, the user's emotions may change due to various factors, or their focus of attention may shift from one area to another. The system continuously monitors these changes and adjusts its output strategy accordingly. For instance, in an online education facial recognition check-in scenario, if the user's attention is initially focused on the facial recognition prompts on the check-in interface, the system will guide the user through the facial recognition operation with clear text and animations. Once check-in is successful, if the system detects that the user's attention has shifted to the course content display area, it can display a concise pop-up message indicating successful check-in without disturbing the user, and provide a link for quick access to the course.

[0078] Through this intelligent output adjustment method based on emotion perception technology, the system of the present invention can provide users with a more personalized, comfortable and efficient interactive experience.

[0079] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A processing system based on facial feature recognition, comprising an image acquisition module, a feature extraction module, a recognition and comparison module, a data storage and management module, and a result output and interaction module; The image acquisition module employs multi-source fusion imaging technology, combining a high-resolution optical sensor and a depth sensing sensor to capture the three-dimensional shape and texture information of a face. It uses infrared-assisted imaging to acquire facial images in low-light environments. The image acquisition quality evaluation index formula is as follows: Q image R represents the image acquisition quality. resolution D represents the image resolution. depth_accuracy S represents the accuracy of depth data. stability F represents image stability. adaptability N represents environmental adaptability. nois Indicates image noise; The feature extraction module employs a quantum neural computing model, which leverages the superposition and entanglement properties of qubits to extract fused information from the microscopic and macroscopic features of the face. This includes structural features of the facial features, dynamic changes in facial muscles, and bioelectrical signal characteristics. The formula for accuracy assessment index is: Where A extract N represents the accuracy of feature extraction. correct_features N represents the number of correctly extracted features. total_features E represents the total number of features to be extracted. quantum_enhancement Indicates the quantum computing enhancement factor; The identification and comparison module employs a chaotic encryption-based matching algorithm, comparing the extracted facial features with features in the database. It leverages the randomness and complexity of chaotic systems to encrypt data transmission and dynamically generate keys during the matching process. The formula for its identification and comparison speed evaluation index is as follows: Where V recognition N represents the recognition and comparison speed. faces_processed T represents the number of faces processed per unit of time. processing Indicates processing time, C chaos_factor Indicates the acceleration factor for chaotic encryption; The data storage and management module combines a blockchain distributed storage architecture with intelligent data compression technology. Its data storage capacity assessment formula is as follows: Where C storage N represents the data storage capacity. compressed_data_stored S represents the actual amount of compressed data stored. storage_space D represents the total space of the storage device. blockchain_efficiency Indicates the blockchain storage efficiency factor; The output and interaction module supports virtual reality feedback, augmented reality visualization guidance, and brain-computer interface thought interaction response. Through AR technology, the recognition results are overlaid on the real scene in the form of virtual tags and displayed to security personnel, or through brain-computer interface devices, users can interact with the system through thought commands.

2. The processing system based on facial feature recognition according to claim 1, characterized in that, The multi-source fusion imaging technology in the image acquisition module supports automatic environmental perception and adaptive parameter adjustment, automatically optimizing imaging parameters according to different environmental factors such as illumination, temperature, and humidity.

3. The processing system based on facial feature recognition according to claim 1, characterized in that, The quantum neural computing model in the feature extraction module uses the quantum annealing algorithm for model optimization training.

4. The processing system based on facial feature recognition according to claim 1, characterized in that, The high-speed matching algorithm based on chaotic encryption in the identification and comparison module supports dynamic key updates and multimodal feature fusion comparison.

5. The processing system based on facial feature recognition according to claim 1, characterized in that, The blockchain distributed storage architecture in the data storage and management module uses smart contracts to automate data access control and securely control data sharing.

6. The processing system based on facial feature recognition according to claim 1, characterized in that, The output method in the result output and interaction module supports user personalization and scene adaptive switching.

7. A processing method based on facial feature recognition, used to implement the processing system based on facial feature recognition as described in any one of claims 1-6, characterized in that, Includes the following steps: Image acquisition: The image acquisition module uses multi-source fusion imaging technology and automatic environmental perception technology to acquire image information containing human faces, and performs preliminary image preprocessing, including noise reduction and contrast enhancement; Feature extraction: The preprocessed image is transmitted to the feature extraction module, where quantum neural computing model and quantum annealing algorithm are used to extract facial features; Recognition and comparison: The extracted facial features are compared with features in the database using a high-speed matching algorithm based on chaotic encryption; Data storage: The recognition results and facial feature data are securely stored using a blockchain distributed storage architecture and intelligent data compression technology; Results Output: The results output and interaction module presents the recognition results to the user in a variety of innovative ways and supports user interaction.

8. The processing method based on facial feature recognition according to claim 7, characterized in that, In the image acquisition step, dynamic tracking technology is used to ensure that the face is always in the imaging position.

9. The processing method based on facial feature recognition according to claim 7, characterized in that, In the feature extraction step, feature enhancement techniques are used to highlight and optimize key features.

10. The processing method based on facial feature recognition according to claim 7, characterized in that, In the output step, emotion perception technology is used to adjust the output method and content according to the user's emotional state and attention focus.

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