[ Huaqiang Security Network News ] Human body recognition should be known to everyone! The main technology of the product includes three parts of “human face detection, human face tracking, human body face comparisonâ€. Let's analyze the principle of human body recognition! 
1. The content of human face recognition technology The human face recognition technology consists of three parts:
(1) Human face detection Face appearance detection refers to judging whether there is a face image in a dynamic scene and a complicated background, and separating the face image. There are generally the following methods:
1 reference template method First, design one or several standard face templates, then calculate the degree of matching between the sample collected by the test and the standard template, and judge whether there is a human face through the threshold;
2 Face rule method Because the face has a certain structural distribution feature, the so-called face rule method extracts these features to generate corresponding rules to determine whether the test sample contains a human face;
3 sample learning method This method uses the artificial neural network method in pattern recognition, that is, the classifier is generated by learning the face image sample set and the non-face image sample set;
4 skin color model method This method is based on the law that the skin color distribution is relatively concentrated in the color space.
5 Feature Sub-Face Method This method treats all face image sets as one face image subspace and determines whether there is a face image based on the distance between the detected sample and its projection between the child holes.
It is worth mentioning that the above five methods can also be comprehensively adopted in the actual detection system.
(2) Body surface tracking Face tracking refers to dynamic target tracking of the detected face. Specifically, a model-based approach or a combination of motion and model is used.
In addition, tracking with skin color models is a simple and effective means.
(3) Human face comparison The face comparison is to identify the detected face image or perform a target search in the face image library. This actually means that the sampled image is compared with the stock image in turn and the best match is found. Therefore, the description of the image determines the specific method and performance of the face recognition. At present, two description methods of feature vector and face pattern template are mainly used:
1 eigenvector method This method first determines the size, position, distance and other attributes of the facial image iris, nose, mouth angle, etc., and then calculates their geometric features, and these feature quantities form a description of the image. Feature vector.
2 face mask method This method is to store a number of standard face image templates or face image organ templates in the library. When performing the comparison, the sample face image is used for all pixels and all templates in the library using normalized correlation metrics. match.
In addition, there is an autocorrelation network using pattern recognition or a combination of features and templates.
The core of human face recognition technology is actually "local body feature analysis" and "graphic/neural recognition algorithm." This algorithm is a method that utilizes various organs and features of the human face. For example, the corresponding geometric relationship multi-data formation identification parameter is compared, judged and confirmed with all the original parameters in the database. Generally, the judgment time is less than 1 second.
2. The process of recognizing the human face is generally divided into three steps:
(1) First establish a facial image of the human face. That is, the camera collects the face image files of the human body of the unit personnel or takes their photos to form the image files, and stores these face image files to generate the faceprint code.
(2) Acquiring the current body image The face image of the current entry and exit person captured by the camera, or taking a photo input, and generating the face image of the current face image file.
(3) Compare the current face code with the file stock. Compare the face code of the current face image with the face code code in the file stock. The above-mentioned "face code coding" method works according to the essential features and the beginning of the face of the human body. This face code is resistant to changes in light, skin tones, facial hair, hair, glasses, expressions and postures, and has a strong reliability that allows it to accurately identify a person from a million people.
The process of recognizing the human face can be done automatically, continuously, and in real time using ordinary image processing equipment.
(1) Human face detection Face appearance detection refers to judging whether there is a face image in a dynamic scene and a complicated background, and separating the face image. There are generally the following methods:
1 reference template method First, design one or several standard face templates, then calculate the degree of matching between the sample collected by the test and the standard template, and judge whether there is a human face through the threshold;
2 Face rule method Because the face has a certain structural distribution feature, the so-called face rule method extracts these features to generate corresponding rules to determine whether the test sample contains a human face;
3 sample learning method This method uses the artificial neural network method in pattern recognition, that is, the classifier is generated by learning the face image sample set and the non-face image sample set;
4 skin color model method This method is based on the law that the skin color distribution is relatively concentrated in the color space.
5 Feature Sub-Face Method This method treats all face image sets as one face image subspace and determines whether there is a face image based on the distance between the detected sample and its projection between the child holes.
It is worth mentioning that the above five methods can also be comprehensively adopted in the actual detection system.
(2) Body surface tracking Face tracking refers to dynamic target tracking of the detected face. Specifically, a model-based approach or a combination of motion and model is used.
In addition, tracking with skin color models is a simple and effective means.
(3) Human face comparison The face comparison is to identify the detected face image or perform a target search in the face image library. This actually means that the sampled image is compared with the stock image in turn and the best match is found. Therefore, the description of the image determines the specific method and performance of the face recognition. At present, two description methods of feature vector and face pattern template are mainly used:
1 eigenvector method This method first determines the size, position, distance and other attributes of the facial image iris, nose, mouth angle, etc., and then calculates their geometric features, and these feature quantities form a description of the image. Feature vector.
2 face mask method This method is to store a number of standard face image templates or face image organ templates in the library. When performing the comparison, the sample face image is used for all pixels and all templates in the library using normalized correlation metrics. match.
In addition, there is an autocorrelation network using pattern recognition or a combination of features and templates.
The core of human face recognition technology is actually "local body feature analysis" and "graphic/neural recognition algorithm." This algorithm is a method that utilizes various organs and features of the human face. For example, the corresponding geometric relationship multi-data formation identification parameter is compared, judged and confirmed with all the original parameters in the database. Generally, the judgment time is less than 1 second.
2. The process of recognizing the human face is generally divided into three steps:
(1) First establish a facial image of the human face. That is, the camera collects the face image files of the human body of the unit personnel or takes their photos to form the image files, and stores these face image files to generate the faceprint code.
(2) Acquiring the current body image The face image of the current entry and exit person captured by the camera, or taking a photo input, and generating the face image of the current face image file.
(3) Compare the current face code with the file stock. Compare the face code of the current face image with the face code code in the file stock. The above-mentioned "face code coding" method works according to the essential features and the beginning of the face of the human body. This face code is resistant to changes in light, skin tones, facial hair, hair, glasses, expressions and postures, and has a strong reliability that allows it to accurately identify a person from a million people.
The process of recognizing the human face can be done automatically, continuously, and in real time using ordinary image processing equipment.
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