[ Huaqiang Security Network News ] I heard that the face recognition attendance machine, have you heard of a face recognition camera , let us look at what is a face recognition camera, and what is its working principle? ! 
Face Recognition Algorithms <br> Generally, face recognition systems include image capture, face localization, image preprocessing, and face recognition (identity confirmation or identity lookup). The system input is generally a series or a series of face images containing unidentified identities, as well as face recognition face images or corresponding codes of several known identities in the face database, and the output is a series of similarities. A score indicating the identity of the face to be identified.
Face Recognition Algorithm Classification <br> Feature-based recognition algorithms.
Based on the entire face recognition algorithm (Appearance-based recognition algorithms).
Template-based recognition algorithms.
Recognition algorithms using neural network.
Based on the theory of illumination estimation model, an illumination preprocessing method based on Gamma gray scale correction is proposed. Based on the illumination estimation model, the corresponding illumination compensation and illumination balance strategies are implemented. Optimized deformation statistical correction theory, based on statistical deformation correction theory, optimize face pose; neural network recognition enhanced iterative theory, enhanced iterative theory is an effective extension of DLFA face detection algorithm; original real-time feature recognition theory, the theory focuses on The intermediate value processing of the real-time data of the face, so that the best matching effect can be achieved between the recognition rate and the recognition performance.
Face Recognition Data <br> Face recognition needs to accumulate data related to a large number of collected face images, which is used to verify algorithms and continuously improve recognition accuracy. These data such as A Neural Network Face Recognition Assignment ), orl face database, MIT Biology and Computational Learning Center face recognition database, Essex University School of Computer and Electronic Engineering face recognition data.
Advantages of using face recognition <br> The advantage of face recognition is its naturalness and characteristics that are not perceived by the individual being tested. The so-called naturalness means that the recognition method is the same as that used by humans (or even other organisms) for individual identification. For example, face recognition, humans also distinguish and confirm the identity by observing the face, in addition to the natural recognition, iris recognition speech recognition, body shape recognition, etc., while fingerprint recognition, iris recognition, etc. are not natural, because human Or other organisms do not distinguish individuals by such biological characteristics.
Undetected features are also important for an identification method, which makes the recognition method unobjectionable and not easily fooled because it is not easily noticeable. Face recognition has this feature. It completely uses visible light to obtain facial image information. Unlike fingerprint recognition or iris recognition, it is necessary to use electronic pressure sensors to collect fingerprints, or use infrared to collect iris images. These special collection methods are easy. Being perceived, it is more likely to be deceived by pretense.
Face Recognition Difficulties <br> Face recognition is considered to be one of the most difficult research topics in the field of biometrics and even artificial intelligence. The difficulty of face recognition is mainly caused by the characteristics of the human face as a biometric feature.
Similarity: The difference between different individuals is small, the structure of all faces is similar, and even the structural shapes of face organs are similar. It is advantageous for positioning with a human face, but it is disadvantageous for distinguishing human subjects by human faces.
Variability: The shape of the face is very unstable. People can produce many expressions through changes in the face. At different viewing angles, the visual images of the faces are also very different. In addition, face recognition is also subject to lighting conditions (such as daytime). And night, indoor and outdoor, etc.), many face coverings (such as masks, sunglasses, hair, beards, etc.), age and other factors.
In face recognition, the first type of change should be scaled up as a criterion for distinguishing individuals, while the second type of change should be eliminated because they can represent the same individual. The first type of change is usually referred to as an inter-class difference, and the second type of change is referred to as an intra-class difference. For human faces, intra-class changes tend to be larger than inter-class changes, making it difficult to distinguish individuals with inter-class changes in the case of interference within the class.
The main purpose of face recognition <br> Face recognition is mainly used for identification. Since video surveillance is rapidly spreading, many video surveillance applications urgently need a fast identification technology in a long-distance, user-incompatible state, in order to quickly confirm the identity of a person at a long distance and realize intelligent early warning. The face recognition camera made by face recognition technology is undoubtedly the best choice for identification. The fast face detection technology can find the face in real time from the surveillance camera image and compare it with the face database in real time. Enable fast identification.
Face Recognition Algorithm Classification <br> Feature-based recognition algorithms.
Based on the entire face recognition algorithm (Appearance-based recognition algorithms).
Template-based recognition algorithms.
Recognition algorithms using neural network.
Based on the theory of illumination estimation model, an illumination preprocessing method based on Gamma gray scale correction is proposed. Based on the illumination estimation model, the corresponding illumination compensation and illumination balance strategies are implemented. Optimized deformation statistical correction theory, based on statistical deformation correction theory, optimize face pose; neural network recognition enhanced iterative theory, enhanced iterative theory is an effective extension of DLFA face detection algorithm; original real-time feature recognition theory, the theory focuses on The intermediate value processing of the real-time data of the face, so that the best matching effect can be achieved between the recognition rate and the recognition performance.
Face Recognition Data <br> Face recognition needs to accumulate data related to a large number of collected face images, which is used to verify algorithms and continuously improve recognition accuracy. These data such as A Neural Network Face Recognition Assignment ), orl face database, MIT Biology and Computational Learning Center face recognition database, Essex University School of Computer and Electronic Engineering face recognition data.
Advantages of using face recognition <br> The advantage of face recognition is its naturalness and characteristics that are not perceived by the individual being tested. The so-called naturalness means that the recognition method is the same as that used by humans (or even other organisms) for individual identification. For example, face recognition, humans also distinguish and confirm the identity by observing the face, in addition to the natural recognition, iris recognition speech recognition, body shape recognition, etc., while fingerprint recognition, iris recognition, etc. are not natural, because human Or other organisms do not distinguish individuals by such biological characteristics.
Undetected features are also important for an identification method, which makes the recognition method unobjectionable and not easily fooled because it is not easily noticeable. Face recognition has this feature. It completely uses visible light to obtain facial image information. Unlike fingerprint recognition or iris recognition, it is necessary to use electronic pressure sensors to collect fingerprints, or use infrared to collect iris images. These special collection methods are easy. Being perceived, it is more likely to be deceived by pretense.
Face Recognition Difficulties <br> Face recognition is considered to be one of the most difficult research topics in the field of biometrics and even artificial intelligence. The difficulty of face recognition is mainly caused by the characteristics of the human face as a biometric feature.
Similarity: The difference between different individuals is small, the structure of all faces is similar, and even the structural shapes of face organs are similar. It is advantageous for positioning with a human face, but it is disadvantageous for distinguishing human subjects by human faces.
Variability: The shape of the face is very unstable. People can produce many expressions through changes in the face. At different viewing angles, the visual images of the faces are also very different. In addition, face recognition is also subject to lighting conditions (such as daytime). And night, indoor and outdoor, etc.), many face coverings (such as masks, sunglasses, hair, beards, etc.), age and other factors.
In face recognition, the first type of change should be scaled up as a criterion for distinguishing individuals, while the second type of change should be eliminated because they can represent the same individual. The first type of change is usually referred to as an inter-class difference, and the second type of change is referred to as an intra-class difference. For human faces, intra-class changes tend to be larger than inter-class changes, making it difficult to distinguish individuals with inter-class changes in the case of interference within the class.
The main purpose of face recognition <br> Face recognition is mainly used for identification. Since video surveillance is rapidly spreading, many video surveillance applications urgently need a fast identification technology in a long-distance, user-incompatible state, in order to quickly confirm the identity of a person at a long distance and realize intelligent early warning. The face recognition camera made by face recognition technology is undoubtedly the best choice for identification. The fast face detection technology can find the face in real time from the surveillance camera image and compare it with the face database in real time. Enable fast identification.
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