In-depth Analysis of Face Liveness Detection Technology in HarmonyOS Next

This article aims to deeply explore the face liveness detection technology in the Huawei HarmonyOS Next system (up to API 12 as of now), and summarize it based on practical 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. Principles and Importance of Face Liveness Detection

(1) In-depth Explanation of the Principles

In the security protection system of HarmonyOS Next, face liveness detection technology is like a solid defense line, and its core principles are based on a variety of advanced technical means.

The principle of action-based liveness detection is to guide users to perform specific actions, such as blinking, nodding, shaking their heads, etc., and then use the camera to capture the dynamic changes of the user's face. The system will analyze the continuity, coordination of these actions, and their matching degree with the preset action templates to determine whether it is a real live person. For example, when the user blinks, the system will detect whether the change in the opening and closing state of the eyes is natural and smooth, and whether the frequency and amplitude of blinking conform to the physiological characteristics of normal humans. This method can effectively resist static attack methods such as photos and videos, because static images or videos cannot simulate real dynamic actions.

Feature analysis is also one of the important means of face liveness detection. It deeply analyzes features such as the texture, skin color, and light reflection of the face to determine whether it has the characteristics of a real live person. A real face has rich texture details, such as pores and wrinkles on the skin, and will show natural reflection and shadow changes under different lighting conditions. However, forged faces (such as paper photos or images displayed on electronic screens) often show obvious differences in these features. For example, the texture of the face in a photo is relatively monotonous, and the light reflection is unnatural. By extracting and analyzing these features, the system can distinguish between real faces and forged faces.

(2) Emphasis on the Importance in Security Applications

In many security-related applications of HarmonyOS Next, face liveness detection technology plays an indispensable role. Take mobile phone unlocking as an example, it provides users with a convenient and secure unlocking method. Compared with traditional password unlocking or pattern unlocking, face liveness detection does not require users to manually enter a password or draw a pattern. Users only need to naturally look at the mobile phone screen, and the system can quickly determine whether it is a real user and unlock the phone. This not only improves the speed and convenience of user unlocking, but more importantly, it effectively prevents others from illegally unlocking the phone by stealing the password or pattern, protecting the user's personal privacy and data security.

In the access control system, face liveness detection technology ensures that only authorized real personnel can enter a specific area. For places such as corporate office areas and residential communities, the security of the access control system is crucial. Face liveness detection can accurately identify the identity of the person entering, preventing criminals from using forged faces (such as photos or videos) to deceive the access control system, and ensuring the safety and order of the place.

(3) Comparison of Different Liveness Detection Technologies

1. Face Liveness Detection Technology Based on Visible Light The advantage is that its principle is relatively simple. It uses a camera to collect face images under visible light and conducts liveness detection by analyzing the image features. The equipment cost is low, and it is easy to implement and integrate into various devices. For example, a basic visible light face liveness detection function can be realized in the front camera of some ordinary smartphones. However, its disadvantage is that the anti-counterfeiting ability is relatively weak, and it is easy to be deceived by attack methods such as high-definition photos and video playback. In poor lighting conditions, the detection accuracy may be affected. For example, in a low-light environment, the clarity of the image and the accuracy of feature extraction will decrease, increasing the risk of misjudgment.
2. Face Liveness Detection Technology Based on Infrared Light This technology uses infrared light to irradiate the face and then conducts liveness detection by analyzing the face features in the infrared image. Its advantage is that it has less dependence on lighting conditions and can still work normally in complex lighting conditions such as dark environments or direct strong light. At the same time, infrared light can penetrate a certain degree of occlusions (such as glasses) to obtain more real face feature information, and has strong anti-counterfeiting ability, which can effectively resist attacks such as photos and videos. However, the equipment cost based on infrared light is relatively high, requiring additional infrared light sources and corresponding sensors, and the technical implementation is more difficult, with high requirements for the hardware of the device.
3. Face Liveness Detection Technology Based on 3D Structured Light By emitting specific structured light (such as laser speckles) and analyzing the pattern of the reflected light, the three-dimensional structure information of the face is obtained, and then liveness detection is carried out. Its advantage is that the detection accuracy is extremely high, and it can accurately identify the three-dimensional structure features of the face, almost able to resist all common forgery attack methods, such as mask attacks, photo attacks, and video attacks. It provides very high security and is suitable for scenarios with extremely high security requirements, such as identity verification in financial institutions and access control in important places. However, the technology based on 3D structured light is costly, the equipment is complex and large in size, and the power consumption is relatively high, which is not suitable for popularization in all devices and is generally applied to high-end devices or professional fields with extremely high security requirements.

II. Implementation of Face Liveness Detection in Core Vision Kit

(1) Explanation of Function Interfaces and Usage Methods

The Core Vision Kit provides developers with a series of powerful face liveness detection function interfaces, making it relatively easy to integrate the face liveness detection function into HarmonyOS Next applications.

First, developers need to initialize the face liveness detection engine through specific interfaces. For example, use the FaceLivenessDetector.create method to create a face liveness detection instance and pass in corresponding configuration parameters, such as the detection mode (action liveness detection mode or silent liveness detection mode), the threshold for liveness detection (used to determine the credibility of liveness), etc.

After initialization is completed, call the detect method to start face liveness detection. This method receives the video frame data collected by the camera as input and then performs face detection and liveness judgment in the background. When a face is detected and the liveness judgment is completed, the system will return the detection result through a callback function, including information such as the position of the detected face and the liveness detection score (used to represent the credibility of liveness, the higher the score, the more likely it is a real live person).

(2) Code Example Demonstration (if possible)

The following is a simplified code example showing how to use the Core Vision Kit to implement the face liveness detection function in a HarmonyOS Next application (assuming that 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 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 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, credibility:', result.livenessScore);
    } else {
        console.log('It may be a non-live person or the liveness credibility 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 judged whether it is a real live person.

(3) Analysis of Detection Accuracy and Performance and Discussion on Optimization

1. Analysis of Factors Affecting Detection Accuracy The face liveness detection accuracy of the Core Vision Kit is affected by many factors. The environmental lighting condition is a key factor. Too strong or too dark lighting may make the face features blurry or produce shadows, affecting the accuracy of feature extraction and liveness judgment. For example, under direct strong light, the face may be overexposed, resulting in the loss of some facial features; in a low-light environment, the camera may not be able to clearly capture the details of the face.

The degree of user action cooperation also affects the accuracy. If the user's actions are not standardized, natural, or complete during action-based liveness detection, the system may misjudge it as a non-live person. For example, if the user blinks too quickly or too slowly, which is significantly different from the preset normal blinking pattern, it may be judged as a non-live person.

In addition, the changes in the face pose and expression may also affect the detection accuracy. When the face is in a state of large-angle tilt or rotation, some facial features may not be completely collected and accurately analyzed, thus affecting the accuracy of liveness detection. Similarly, extreme expressions (such as laughing or crying loudly) may change the shape and texture features of the face, posing certain challenges to the detection.

1. Discussion on Factors Affecting Performance In terms of performance, the hardware performance of the device has an important impact on the face liveness detection speed. Devices with lower performance may experience delays when processing video frame data and running the liveness detection algorithm, resulting in a slower detection speed. For example, a device with a weak CPU processing power may not be able to process a large amount of image data in a timely manner, making the detection process laggy.

At the same time, the complexity of the algorithm also affects the performance. If the adopted liveness detection algorithm has a large computational amount and requires a lot of computing resources and time, it may not achieve the desired performance on devices with limited resources. For example, some high-precision 3D structured light liveness detection algorithms have high accuracy but also high requirements for the GPU performance of the device. If the device does not have a powerful enough GPU, the detection speed may be significantly reduced.

1. Proposed Optimization Methods For the optimization of detection accuracy, the method of multi-modal information fusion can be adopted. In addition to analyzing the visual features of the face, other modal information can be combined, such as voice information (collecting voice at the same time when the user performs action-based liveness detection, and judging the synchronicity and authenticity of the voice and the action), bioelectrical signals (such as detecting the changes in the electrical activity of facial muscles), etc., to improve the accuracy of liveness detection. At the same time, by optimizing the adaptability of the algorithm to lighting changes, an adaptive lighting compensation technology can be adopted to accurately extract face features under different lighting conditions. For example, automatically adjust the contrast and brightness of the image according to the environmental lighting intensity to enhance the distinguishability of face features.

For performance optimization, optimization can be carried out at the algorithm level. Adopt lightweight deep learning models or optimize traditional algorithms to reduce the computational amount. For example, prune and quantize the neural network model to reduce the number of model parameters and computational complexity and improve the running speed on devices with limited resources. At the same time, make reasonable use of the hardware acceleration function of the device, such as allocating some computing tasks to the GPU or NPU for execution (if the device supports it) to improve the detection efficiency. In terms of data processing, adopt a caching mechanism to reduce repeated data processing operations and improve the overall performance of the system.

III. Applications and Future Development Trends of Face Liveness Detection

(1) Discussion on Application Scenarios

1. Expansion of Mobile Phone Unlocking Applications In HarmonyOS Next mobile phones, face liveness detection technology provides a more secure and convenient way for mobile phone unlocking. Users only need to pick up the mobile phone and look at the screen, and the system can quickly complete liveness detection and unlock the phone. Compared with traditional fingerprint unlocking, face liveness detection is not affected by factors such as wet or oily fingers, and it is more convenient to use. Moreover, in some special scenarios, such as when the user is carrying items in both hands or wearing gloves, face liveness detection can still work normally, providing users with a seamless unlocking experience. In addition, by combining with other security technologies (such as passwords, fingerprints, etc.), multi-modal identity verification can be achieved, further improving the security of mobile phone unlocking. For example, when the face liveness detection score is lower than a certain threshold, the user can be required to enter a password or use a fingerprint for secondary verification to ensure that only legitimate users can unlock the phone.
2. Deepening of Access Control System Applications In the field of access control systems, the face liveness detection technology of HarmonyOS Next can achieve more intelligent and efficient access control management. In addition to the traditional access control function, it can be deeply integrated with the enterprise's attendance system, visitor management system, etc. For example, when an employee passes through the access control, face liveness detection is not only used for identity verification but can also record the attendance time at the same time, realizing automated attendance management. For visitor management, after the visitor registers through face liveness detection at the access control, the system can automatically assign temporary permissions to them and automatically invalidate them after the visit ends, improving the efficiency and security of access control management. At the same time, through the network connection, the access control system can upload the personnel entry and exit information to the management center in real time, facilitating managers to monitor and conduct statistical analysis.

(2) Analysis of Challenges Faced and Solutions

1. Challenges in Improving Anti-counterfeiting Ability and Countermeasures One of the main challenges faced by current face liveness detection technology is how to further improve the anti-counterfeiting ability to deal with increasingly complex forgery means. With the development of technology, forgery technology of faces is also constantly upgrading, such as using more realistic 3D masks and dynamic faces displayed on high-definition screens. In order to deal with these challenges, a combination of multiple technical means can be adopted. For example, in addition to the multi-modal information fusion mentioned above, strengthen the analysis and recognition of the subtle features of the face (such as pores, capillaries, etc.), which are difficult to perfectly simulate in forged faces. At the same time, use deep learning technology to continuously learn and identify newly emerging forgery patterns, update the detection algorithm in a timely manner, and improve the ability to resist new forgery means.
2. Problems of Adaptability to Complex Environments and Solutions The adaptability to complex environments is also an important issue that needs to be solved by face liveness detection technology. Under different environmental factors such as lighting conditions, temperature, and humidity, the performance of the face liveness detection system may be affected. In response to changes in lighting conditions, a more intelligent lighting compensation algorithm can be adopted, such as a lighting estimation and compensation method based on deep learning, which can adjust the detection strategy in real time according to changes in environmental lighting to ensure accurate detection under various lighting conditions. For factors such as temperature and humidity, select hardware devices that are adaptable to different environmental conditions, and optimize and calibrate the devices to ensure their stability and reliability in different environments. In addition, through a large number of field tests and data collection, continuously optimize the adaptability of the algorithm to different environmental factors and improve the overall performance of the system in complex environments.

(3) Outlook on Future Development Trends

Looking to the future, the face liveness detection technology in HarmonyOS Next will develop in the direction of greater intelligence, high precision, and multi-modal integration. With the continuous progress of artificial intelligence technology, deep learning algorithms will play a more important role in face liveness detection. For example, by learning more complex face feature representations through deeper neural network architectures, the detection accuracy will be further improved. At the same time, multi-modal information fusion will become the norm. In addition to the existing visual, voice, and other modalities, more physiological features (such as heart rate, respiration, etc.) may be combined for liveness detection, providing more comprehensive and accurate identity verification. In terms of hardware, with the continuous popularization and performance improvement of 3D cameras, infrared cameras, and other devices, face liveness detection technology will be able to obtain more abundant and accurate face information, providing strong support for high-precision liveness detection. In addition, face liveness detection technology will also be combined with other emerging technologies (such as blockchain technology for the secure storage and verification of identity information) to create a more secure, intelligent, and convenient identity verification ecosystem, which will be widely applied in various fields such as finance, security, and transportation, providing more reliable security guarantees for people's lives and social development. It is hoped that through the introduction of this article, everyone can have a deeper understanding of the face liveness detection technology in HarmonyOS Next and can better apply this technology in practical development to contribute to the security of the system. If you encounter other problems in the practice process, you are welcome to communicate and discuss together! Haha!