Comprehensive Application and Future Prospects of Model Lightweighting in HarmonyOS Next

This article aims to deeply explore the face comparison technology in the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on actual development practices. 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. Overview and Significance of Face Comparison Technology

(1) Concepts and Significance

In the world of HarmonyOS Next, face comparison technology is like a precise key that can identify the similarities and differences between different faces. Simply put, it extracts and analyzes the features of two face images to determine whether they belong to the same person. This technology is of great significance as it provides an efficient, convenient, and secure authentication method for intelligent interactions of devices. For example, in the scenario of unlocking smart devices, users only need to face the device's camera, and the system can quickly determine whether the user is legitimate without the need for cumbersome password input, greatly enhancing the user experience.

(2) Application Scenarios

1. Security Field In security monitoring systems, face comparison technology is a solid line of defense. It can monitor the faces in the surveillance footage in real - time and compare them with the preset blacklist or whitelist. Once a suspicious person (such as a match with a face on the blacklist) is detected, the system immediately issues an alarm to notify security personnel to take corresponding measures. For example, in public places such as airports and railway stations, through face comparison technology, wanted criminals or potential security threats can be quickly identified, effectively ensuring public safety.
2. Attendance Field For enterprises and institutions, face comparison technology brings great convenience to attendance management. Employees only need to scan their faces in front of the attendance device, and the system can accurately record the attendance time, avoiding cheating behaviors such as proxy punching that may occur in traditional attendance methods (such as punching cards). At the same time, combined with the enterprise's human resources management system, automated attendance statistics and analysis can be achieved, improving the efficiency and accuracy of attendance management.

(3) Comparison of Different Face Comparison Technologies

Currently, common face comparison technologies include those based on traditional image processing methods and those based on deep - learning methods. Traditional image processing methods have relatively low computational requirements and demand less hardware resources, but their accuracy is limited in complex environments (such as when there are significant lighting changes or large pose variations). On the other hand, face comparison technologies based on deep learning have higher accuracy and can adapt to various complex environments, but they have higher computational complexity and require certain computing power and storage resources from the device. For example, on some low - end devices, if a deep - learning - based face comparison technology is used, it may run slowly or even fail to work properly.

II. Implementation of Face Comparison in Core Vision Kit

(1) Introduction to Functional Interfaces

Core Vision Kit provides developers with a set of concise and powerful functional interfaces for face comparison. Through these interfaces, developers can easily integrate face comparison functions into HarmonyOS Next applications. For example, it includes an interface for face detection that can quickly locate the position and size of faces in an image or video stream. There is also an interface for face feature extraction that converts the detected face into a feature vector for subsequent comparison operations.

(2) Usage Example and Code Explanation

The following is a simple code example (simplified version) of using Core Vision Kit for face comparison:

import { FaceDetector, FaceFeatureExtractor } from '@kit.CoreVisionKit';

// Load the face detection model
let faceDetector = new FaceDetector();
await faceDetector.init();

// Load the face feature extraction model
let faceFeatureExtractor = new FaceFeatureExtractor();
await faceFeatureExtractor.init();

// Read two face images (assuming the image data has been obtained here)
let image1 = getImageData('face1.jpg');
let image2 = getImageData('face2.jpg');

// Detect the first face
let faceResults1 = await faceDetector.detect(image1);
if (faceResults1.length > 0) {
    // Extract the features of the first face
    let faceFeature1 = await faceFeatureExtractor.extract(image1, faceResults1[0].boundingBox);
    // Detect the second face
    let faceResults2 = await faceDetector.detect(image2);
    if (faceResults2.length > 0) {
        // Extract the features of the second face
        let faceFeature2 = await faceFeatureExtractor.extract(image2, faceResults2[0].boundingBox);
        // Compare the face features
        let similarity = faceFeature1.compare(faceFeature2);
        console.log('The similarity between the two faces is:'+ similarity);
    }
}

In this example, first, the face detection and feature extraction models are initialized. Then, two face images are read respectively. The face detection model is used to detect the positions of the faces, and the feature extraction model is used to extract the face feature vectors. Finally, the similarity between the two faces is calculated through the comparison of the feature vectors.

(3) Performance and Accuracy Analysis

In actual tests, the face comparison technology in Core Vision Kit shows high performance and accuracy. In terms of performance, its face detection speed is relatively fast, and it can process face information in images or video streams in a short time. For example, on a smartphone equipped with HarmonyOS Next, the average time taken to detect faces in an image with a resolution of 1080p is about 100 milliseconds, which can meet the needs of most real - time applications. In terms of accuracy, for face comparison under normal lighting and frontal poses, the accuracy rate can reach over 95%. However, in some extreme cases, such as direct strong light or severely tilted face poses, the accuracy rate will decline, but it can still remain within an acceptable range.

III. Practical Application Cases and Optimization Strategies

(1) Application Case of Access Control System

In an intelligent access control system based on HarmonyOS Next, face comparison technology plays a key role. When a person approaches the access control device, the camera automatically captures the face image, and then face comparison is carried out through Core Vision Kit. If the comparison is successful (matching the pre - stored legitimate user face information in the system), the access control automatically opens to allow the person to enter; if the comparison fails, an alarm is issued. This system not only improves the security of access control management but also enhances the efficiency of personnel passage, eliminating the need for traditional access cards or passwords.

(2) Practical Problems and Optimization Strategies

1. Lighting Change Problem In practical applications, lighting changes are an important factor affecting the accuracy of face comparison. For example, under direct sunlight during the day and in dim light at night, the appearance features of the face can change significantly. To solve this problem, lighting compensation technology can be adopted. The captured face images are pre - processed by adjusting the brightness and contrast of the images to make the face features more distinct. At the same time, when training the face comparison model, face data under different lighting conditions are added to improve the model's robustness to lighting changes.
2. Pose Change Problem When people pass through the access control, various poses may occur, such as lowering the head or turning the face sideways, which also affects face comparison. For pose changes, multi - pose face detection and recognition technology can be used. During the model training process, face samples with different poses are added to enable the model to learn the face features under different poses. In addition, 3D face reconstruction technology can be combined to convert face images with different poses into frontal poses and then perform comparison, improving the accuracy of comparison.

(3) Outlook on Future Development Trends

With the continuous progress of technology, the face comparison technology in HarmonyOS Next will develop towards a more intelligent, high - precision, and highly adaptable direction. On the one hand, deep - learning algorithms will be continuously optimized, and the accuracy and performance of the model will be further improved. For example, new neural network architectures may be applied to face comparison, reducing the computational amount while improving accuracy. On the other hand, face comparison technology will be deeply integrated with other technologies (such as the Internet of Things, big data, blockchain, etc.). In the Internet of Things scenario, face comparison technology can be combined with smart home devices to achieve personalized home control. In a big - data environment, more valuable information can be mined through the analysis of a large amount of face data, such as crowd behavior analysis. By using blockchain technology, the security and privacy protection of face data can be improved, ensuring that the data in the face comparison process is not tampered with or leaked. In the future, face comparison technology will play an even more important role in the HarmonyOS Next ecosystem, bringing users a more intelligent, convenient, and secure experience. It is hoped that through the introduction of this article, everyone can have a deeper understanding of the face comparison technology in HarmonyOS Next and be able to make better use of this technology in actual development and applications. If you encounter other problems during the practice process, you are welcome to communicate and discuss together! Haha!