Is AI face detection and face recognition a concept? What's the difference?

Face detection - Also known as face detection - It is based on artificial intelligence (AI) Computer technology , It is used to find and recognize faces in digital images . Face detection technology can be applied in various fields —— Including safety 、 Biometrics 、 Law enforcement 、 Entertainment and personal safety —— To provide real-time monitoring and tracking of personnel .

Face detection has developed from basic computer vision technology to machine learning (ML) Progress , Then to the increasingly complex artificial neural network (ANN) And related technologies ; The result is continuous performance improvement . Now? , It plays an important role as the first step in many key applications —— Including face tracking 、 Face analysis and face recognition . Face detection has a significant impact on the execution of sequential operations in applications .

In face analysis , Face detection helps to identify which parts of the image or video should be concerned , To use facial expressions to determine age 、 Gender and mood . In the face recognition system —— It mathematically maps individual facial features and stores the data as facial impressions —— The algorithm requires face detection data to identify which parts of the image or video are needed to generate facial imprints . Once identified , The new facial imprint can be compared with the stored facial imprint to determine whether there is a match .

How face detection works

Face detection applications use algorithms and machine learning to find faces in larger images , These images usually contain other non face objects , Like the scenery 、 Buildings and other human parts , Such as feet or hands . Face detection algorithms usually start with searching human eyes —— This is one of the easiest features to detect . Then the algorithm may try to detect eyebrows 、 mouth 、 nose 、 Nostrils and iris . Once the algorithm comes to a conclusion, it has found a facial region , It will apply additional tests to confirm that it has actually detected a face .

To help ensure accuracy , The algorithm needs to be trained on a large data set containing hundreds of thousands of positive and negative images . The training improves the ability of the algorithm to determine whether there is a face and its position in the image .

The methods used in face detection can be knowledge-based 、 feature-based 、 Template matching or appearance based . Each has its own advantages and disadvantages ：

• Knowledge-based or rule-based methods describe faces according to rules . The challenge of this approach is that it is difficult to put forward clearly defined rules .
• Feature invariant method —— Use features such as a person's eyes or nose to detect the face —— May be negatively affected by noise and light .
• Template matching method Based on comparing the image with a previously stored standard face pattern or feature , And associate the two to detect the face . Unfortunately , These methods do not solve the problem of posture 、 Changes in scale and shape .
• Appearance based approach Statistical analysis and machine learning are used to find the relevant features of face images . This method is also used for feature extraction of face recognition , It is divided into sub methods .

Some of the more specific techniques used in face detection include ：

• Remove the background . for example , If the image has a solid monochrome background or a predefined static background , Then removing the background helps to display the face boundary .
• In color images , Sometimes you can use skin color to find faces ; However , This may not apply to all skin colors .
• Using motion to find faces is another option . In real-time video , The face is almost always moving , Therefore, the user using this method must calculate the moving area . One disadvantage of this method is the risk of confusion with other objects moving in the background .

The combination of the strategies listed above can provide a comprehensive face detection method .

Due to posture 、 expression 、 Location and direction 、 Skin color and pixel value 、 The presence of glasses or facial hair and camera gain 、 Variability of factors such as differences in lighting conditions and image resolution , Detecting faces in a picture can be complex . In recent years , Progress has been made in face detection using deep learning , Its advantages are significantly better than the traditional computer vision methods .

Major improvements in face detection methods appear in 2001 year , At that time, computer vision researchers Paul Viola and Michael Jones A framework is proposed , Real time face detection with high accuracy .Viola-Jones The framework is based on training model to understand what face is , What is not a face . After training , The model extracts specific features , These features are then stored in a file , So that the features in the new image can be compared with the previously stored features at various stages . If the studied image passes each stage of feature comparison , Then the face has been detected and the operation can continue .

Even though Viola-Jones Frameworks are still popular for face recognition in real-time applications , But it has limitations . for example , If a face is covered with a mask or scarf , Or if a face is not oriented correctly , The framework may not work , Then the algorithm may not find it .

To help eliminate Viola-Jones The shortcomings of the framework and improve face detection , Other algorithms have been developed —— For example, region based convolutional neural network (R-CNN) And a single lens detector (SSD)—— To help improve the process .

Convolutional neural networks (CNN) It is an artificial neural network for image recognition and processing , Dedicated to processing pixel data . R-CNN stay CNN Generate regional proposals on the framework , To locate and classify the objects in the image .

Although the method based on regional proposal network （ Such as R-CNN） Need two shots —— A for generating regional proposals , The other is used to detect each proposed object —— but SSD Only one shot is needed to detect multiple objects in the image . therefore ,SSD Obviously faster than R-CNN.

The advantages of face detection technology

As a key element of face imaging applications such as face recognition and face analysis , Face detection creates various advantages for users , Include ：

• Improved security . Face detection can improve security monitoring and help track criminals , Ensure and enhance the safety of public places .
• Easy to integrate . Face detection and face recognition technologies are easy to integrate , Most solutions are compatible with most security software .
• Automatic identification . Before , Recognition is done manually , Low efficiency , And often inaccurate .AI Face detection technology allows automatic recognition process , This saves time and improves accuracy .

Disadvantages of face detection technology

Although face detection provides users with several benefits , But it also has various disadvantages , Include ：

• Massive data storage burden . Used in face detection ML Technology requires powerful data storage , But not all users can use .
• Detection is susceptible to interference . Although face detection provides more accurate results than manual recognition process , But it is also more easily disturbed by changes in appearance or camera angle .
• Potential privacy breaches . Face detection technology provides great help to assist public security departments in tracking criminals . However , Because the development threshold is not high , Technology abuse also has the problem of privacy disclosure , Strict regulations must be formulated , To ensure fair use of the technology and compliance with human privacy .

Face detection and face recognition

Although the terms face detection and face recognition are often used together , But face recognition is only an application of face detection —— Although it is one of the most important applications . Facial recognition is used to unlock mobile phones and mobile applications, as well as biometric verification . Bank 、 The retail and transportation security industry uses facial recognition to reduce crime and prevent violence .

In short , The term face recognition goes beyond detecting the presence of a face to determine whose face it is . This process uses a computer application to capture a digital image of an individual's face —— Sometimes it is obtained from video frames —— And compare it with the image stored in the record database .

AI Application of face detection technology

Although all face recognition systems use face detection , But not all face detection systems are used for face recognition . Face detection can also be used for facial motion capture , Or the process of electronically converting human facial movements into a digital database using a camera or laser scanner . The database can be used for movies 、 Games or avatars make realistic computer animation .

Face detection can also be used for autofocus cameras or to calculate the number of people entering an area . The technology also has marketing applications —— for example , Display a specific advertisement when a specific face is recognized .

Another application of face detection is as part of the implementation of emotional reasoning software , for example , It can be used to help autistic people understand the feelings of people around them . The program uses advanced image processing “ Read ” The emotion of the face .

Another use is from visual cues or “ Lip reading ” Language reasoning . This can help the computer determine who is talking , This may help secure applications . Besides , Face detection can be used to help determine which parts of the image need to be blurred to ensure privacy .

At present ,AI Artificial intelligence and machine learning have the most landing scenes in the field of security , Such as ： Security monitoring 、 Face detection in video 、 Face recognition 、 Traffic statistics, etc , It is widely used in residential areas 、 Intelligent access control of buildings , Suspicious personnel around the perimeter are detected 、 Statistics of tourist flow in the scenic spot, etc .

TSINGSEE Qingxi video is based on years of technical experience in the video field , take AI testing 、 Intelligent recognition technology is integrated into various application scenarios , Typical examples are EasyCVR Video convergence cloud service , have AI Face recognition 、 License plate recognition 、 Voice talk 、 Pan tilt control 、 Audible and visual alarm 、 The ability of monitoring video analysis and data collection .