Practical Application of Face Liveness Detection and Card Certificate Recognition in the Smart Security System of HarmonyOS Next

This article aims to deeply explore the practical application of face liveness detection and card certificate recognition technologies in building a smart security system based on the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on practical development experience. It mainly serves as a vehicle for technical sharing and communication. There may be mistakes and omissions. Colleagues are welcome to put forward valuable opinions and questions so that we can make progress together. This article is original content, and any form of reprint must indicate the source and the original author.

I. Requirements and Architecture Planning of the Smart Security System

(1) Detailed Sorting of Functional Requirements

1. Requirements for Access Control In the smart security system, access control is a key function. Through face liveness detection and card certificate recognition technologies, precise control over the access rights of personnel is achieved. Only authorized personnel, when the face liveness detection confirms that they are a real live person and the card certificate recognition information matches, can enter a specific area. For example, in the access control system of an enterprise office building, employees need to swipe their work permits (card certificate recognition) and undergo face liveness detection. Only when both verifications are passed will the access control be opened. This can not only prevent unauthorized personnel from entering but also effectively prevent the use of forged cards, certificates, photos, videos, and other means of deception to pass through the access control, ensuring the safety of the office area.
2. Requirements for Personnel Identity Verification In addition to access control, personnel identity verification also has a wide range of applications in the security system. In some important places, such as data centers, finance offices, etc., strict identity verification is required for the personnel entering. Face liveness detection combined with card certificate recognition can provide dual protection. When a person enters, the system first conducts face liveness detection to ensure that it is a real person, and then obtains detailed personnel information through card certificate recognition and compares it with the pre-stored information of authorized personnel. This dual verification method greatly improves the accuracy and security of identity verification and effectively prevents security risks such as identity theft.

(2) Architecture Design Based on HarmonyOS Next

1. Rational Selection of Hardware Devices
   • High-definition Camera: As an important data acquisition device for face liveness detection and card certificate recognition, the selection of a high-definition camera is crucial. The resolution of the camera should be high enough to clearly capture facial features and the text and image information on the card certificate. For example, choosing a camera with a resolution of 1080p or higher can accurately identify the details of the face, such as the opening and closing of the eyes, facial expressions, etc., which is crucial for face liveness detection; at the same time, it can also clearly capture the small fonts and fine patterns on the card certificate to ensure the accuracy of card certificate recognition. In addition, the frame rate of the camera should also meet the real-time requirement. Generally, a camera with a frame rate of 30fps or higher is selected to ensure that the image data can be accurately collected during the movement of personnel.
   • Identity Recognition Terminal: The identity recognition terminal is the core hardware device of the entire system. It integrates the computing power for processing face liveness detection and card certificate recognition algorithms, as well as the function of communicating with other system components. Select a terminal device with relatively strong performance, such as a device equipped with a high-performance CPU, sufficient memory, and large-capacity storage. A powerful CPU can quickly process image data, run complex liveness detection and card certificate recognition algorithms, and reduce the waiting time of personnel. Sufficient memory is used to store data and algorithm models during operation to ensure the smooth operation of the system. Large-capacity storage is used to save the personnel information database, recognition records, and other data.
2. Clear Division of Software Modules
   • Liveness Detection Module: This module is mainly responsible for implementing the face liveness detection function. Use the relevant interfaces and algorithms provided by Core Vision Kit to conduct real-time detection of the face images collected by the camera. By analyzing the face movements (such as blinking, nodding, shaking the head, etc.), texture features, and light reflection information, it is determined whether it is a real live person. Inside the module, it can be further divided into sub-modules such as image acquisition sub-module, feature extraction sub-module, and liveness judgment sub-module, each of which is responsible for a specific function to improve the maintainability and scalability of the module.
   • Card Certificate Recognition Module: Focus on the implementation of the card certificate recognition function. Process the card certificate images captured by the camera and extract the key information on the card certificate, such as the ID number, name, photo, expiration date, etc. (for different types of card certificates, extract the corresponding key information). This module includes sub-modules such as image preprocessing sub-module (such as grayscale, noise reduction, binarization, etc.), character segmentation sub-module, character recognition sub-module, and information assembly sub-module. Through the collaborative work of these sub-modules, the accurate recognition and information extraction of the card certificate are realized.
   • Data Management Module: Responsible for managing all the data in the system, including the personnel information database, recognition record database, system configuration information, etc. Provide functions such as data storage, query, update, and deletion. For example, when a new employee joins the company, store their personnel information (including name, employee number, photo, card certificate information, etc.) in the personnel information database; every time a person passes through the access control or undergoes identity verification, record the recognition time, result, and other information in the recognition record database. At the same time, the data management module is also responsible for data interaction with other systems (such as the attendance system, visitor management system, etc.) to achieve data sharing and synchronization.

(3) Technical Integration to Ensure the Performance of the Security System

In the system architecture, face liveness detection and card certificate recognition technologies are integrated in the following ways to ensure the security and accuracy of the security system.

When a person approaches the access control or enters the identity verification area, the high-definition camera collects face images and card certificate images (if card certificate verification is required) at the same time. The face image is first transmitted to the liveness detection module, and the liveness detection module uses Core Vision Kit for real-time detection. If the face is detected as a real live person, the liveness detection module sends a signal that the liveness detection is passed to the system and transmits the face feature data (such as the face vector) to the subsequent identity verification module. At the same time, the card certificate image is transmitted to the card certificate recognition module, and the card certificate recognition module recognizes and extracts the information of the card certificate. The extracted card certificate information is also transmitted to the identity verification module. The identity verification module compares the received face feature data and card certificate information with the personnel information database in the data management module. If both match and the person has the corresponding authority, the system sends a door opening signal or an instruction to allow entry; if they do not match or the liveness detection fails, the system sends an alarm signal to prevent the person from entering. Through this close technical integration, the collaborative work of face liveness detection and card certificate recognition is realized, effectively improving the security and accuracy of the security system.

II. Development of Key Functions and Technological Innovation

(1) Implementation and Optimization of the Face Liveness Detection Function

1. Implementation Process Using Core Vision Kit The following is a simplified code example to show how to implement the face liveness detection function using Core Vision Kit (assuming the relevant interfaces and classes have been correctly imported):

import { FaceLivenessDetector } from '@kit.CoreVisionKit';

// Create a face liveness detection instance
let livenessDetector = FaceLivenessDetector.create({
    mode: 'action', // Set it to the action liveness detection mode
    threshold: 0.8 // Set the liveness detection threshold to 0.8
});

// Assume that the camera video frame data has been obtained (here it is simplified as a simulated video frame object videoFrame)
let videoFrame = getVideoFrame();

// Start face liveness detection
livenessDetector.detect(videoFrame).then((result) => {
    if (result.livenessScore >= 0.8) {
        console.log('A real live person is detected, confidence level:', result.livenessScore);
    } else {
        console.log('It may be a non-live person or the liveness confidence level is low');
    }
});

In this example, first, a face liveness detection instance in the action liveness detection mode is created, and the liveness detection threshold is set to 0.8. Then, the camera video frame data is obtained (the data acquisition process is simplified here), and the detect method is called for detection. According to the liveness score in the detection result, it is determined whether it is a real live person.

1. Technical Points and Code Examples for Improving Detection Reliability To improve the reliability of face liveness detection, it can be optimized by combining the technical points in the document. For example, in the image acquisition stage, ensure that the focal length of the camera is accurate and a clear face image is acquired. This can be achieved through an autofocus algorithm or a manual focus assist function. The following is a simple autofocus code example (assuming the relevant functions exist in the camera control library):

import { CameraControl } from '@ohos.cameracontrol';

// Initialize the camera
let camera = CameraControl.init();

// Start autofocus
camera.autoFocus().then(() => {
    console.log('Autofocus completed');
});

In the feature extraction stage, adopt more advanced feature extraction algorithms, such as deep learning-based face feature extraction algorithms, which can more accurately extract the key features of the face and improve the accuracy of liveness detection. At the same time, increase the number and diversity of liveness detection samples. For example, collect live samples under different lighting conditions, different postures, and different expressions for training, so that the model can better adapt to various actual situations. In the liveness judgment stage, combine multiple judgment methods, such as considering the results of both action liveness detection and texture feature analysis for comprehensive judgment. For example, it can be set that only when both the action liveness detection score and the texture feature analysis score exceed a certain threshold, it is determined as a real live person.

(2) Development and Demonstration of the Card Certificate Recognition Function

1. Implementation Methods for Accurately Recognizing Multiple Types of Card Certificates Although the specific card certificate recognition development library is not clearly mentioned in the document, we can assume that there is a similar function library (similar to Tesseract OCR on other platforms). The following is a simplified conceptual code example to show the basic process of ID card recognition (assuming libraries and functions):

import { CardRecognitionLibrary } from '@ohos.cardrecognition';

// Load the ID card image (assuming the image file path has been obtained)
let idCardImagePath = 'id_card.jpg';
let idCardImage = CardRecognitionLibrary.loadImage(idCardImagePath);

// Image preprocessing (assuming the library provides corresponding preprocessing functions)
let preprocessedImage = CardRecognitionLibrary.preprocessImage(idCardImage);

// ID card information extraction
let idCardInfo = CardRecognitionLibrary.extractIdCardInfo(preprocessedImage);

console.log('Name on the ID card:', idCardInfo.name);
console.log('ID number on the ID card:', idCardInfo.idNumber);
// The output of other information is omitted

For other types of card certificates (such as vehicle driving licenses, driver's licenses, etc.), the recognition process is similar, but the logic of information extraction needs to be adjusted according to the specific format and information layout of the card certificate. For example, a vehicle driving license needs to extract information such as the license plate number, vehicle type, and owner's name, and a driver's license needs to extract information such as the driver's license number, driving license type, and expiration date.

1. Information Extraction and Code Snippet Demonstration In the card certificate recognition process, information extraction is a key step. The following is an information extraction code snippet continuing with the above ID card recognition example (assuming the relevant functions exist in the CardRecognitionLibrary):

// Assume that preprocessedImage is the preprocessed ID card image
// Locate the name area (assuming the name on the ID card is in a specific position in the image or found through character segmentation)
let nameRegion = CardRecognitionLibrary.findNameRegion(preprocessedImage);
let name = CardRecognitionLibrary.recognizeText(nameRegion);

// Locate the ID number area
let idNumberRegion = CardRecognitionLibrary.findIdNumberRegion(preprocessedImage);
let idNumber = CardRecognitionLibrary.recognizeText(idNumberRegion);

Through these code snippets, it shows how to extract key information from the preprocessed card certificate image to realize the card certificate recognition function.

(3) Application of Multimodal Information Fusion Technology

1. Elaboration on the Principle of the Fusion Algorithm The multimodal information fusion technology in the smart security system improves the accuracy of identity verification by combining face features and card certificate information. Its principle is to integrate and analyze information from different modalities (face and card certificate). For face features, representative feature vectors are extracted through a deep learning model, and this vector can represent the unique features of the face, such as the shape of the facial features, positional relationships, and facial texture. For card certificate information, the extracted text information (such as name, ID number, etc.) is encoded and converted into a digital form. Then, a fusion algorithm is used to fuse the face feature vector and the card certificate information encoding. Common fusion algorithms include the feature concatenation method, that is, directly concatenating the face feature vector and the card certificate information encoding together to form a new feature vector, and then inputting it into a classifier or verification model for identity judgment; another method is decision-level fusion, which makes independent judgments on face features and card certificate information respectively, and then fuses the two judgment results according to certain rules (such as weighted average) to obtain the final identity verification result.
2. Implementation Details and Code Examples (Conceptual) The following is a simple conceptual code example to show the implementation of multimodal information fusion in identity verification (assuming the relevant functions and classes already exist):

import { FaceFeatureExtractor } from '@kit.CoreVisionKit';
import { CardRecognitionLibrary } from '@ohos.cardrecognition';

// Assume that the face image faceImage and the card certificate image cardImage have been obtained
// Extract face features
let faceFeatureExtractor = new FaceFeatureExtractor();
let faceFeature = faceFeatureExtractor.extract(faceImage);

// Recognize card certificate information and encode it
let cardInfo = CardRecognitionLibrary.extractCardInfo(cardImage);
let cardInfoEncoding = encodeCardInfo(cardInfo); // Assume there is an encoding function

// Feature concatenation-based fusion method (example)
let fusedFeature = concatFeatures(faceFeature, cardInfoEncoding); // Assume there is a concatenation function

// Conduct identity verification (assume there is a verification function)
let verificationResult = verifyIdentity(fusedFeature);

if (verificationResult) {
    console.log('Identity verification passed');
} else {
    console.log('Identity verification failed');
}

In actual implementation, according to specific application requirements and data characteristics, appropriate fusion algorithms and parameter settings need to be selected to achieve the best identity verification effect. Through multimodal information fusion technology, the advantages of face liveness detection and card certificate recognition can be fully utilized to improve the accuracy and reliability of the smart security system and effectively prevent security risks such as identity theft. It is hoped that through the introduction of this article, it can provide some useful references and lessons for developers in the smart security field and jointly promote the development of smart security technologies. If you encounter other problems in the practice process, you are welcome to communicate and discuss together! Haha!