Face recognition technology refers to the use of computer technology for analysis and comparison to recognize faces. Face recognition is a popular computer technology research field, including face tracking detection, automatic adjustment of image zoom, night infrared detection, automatic adjustment of exposure intensity and other technologies.
Face recognition technology belongs to biometric recognition technology, which distinguishes individuals from the biological characteristics of the organism (generally refers to the person).
Face recognition technology is based on the facial features of the person, the input face image or video stream. First, determine whether there is a human face. If there is a human face, it is further called the location, size, and location information of each major facial organ. And based on this information, further extract the identity features contained in each face, and compare it with known faces to identify the identity of each face.
The broad sense of face recognition actually includes various related technologies of the built-in face recognition system, including face image collection, face positioning, face recognition prevention, identity verification and identity search, etc.; while the narrow sense of face recognition specifically refers to passing people The technology or system for face verification or identity search.
Face recognition technology consists of three parts:
(1) Face detection
Face detection refers to judging whether there is a face image in a dynamic scene and a complex background, and separating the face image. There are generally the following methods:
①Reference template method
First design one or several standard face templates, then calculate the degree of matching between the sample collected in the test and the standard template, and use the threshold to determine whether there is a face;
②Face rule method
Since human faces have certain structural distribution characteristics, the so-called face rule method extracts these characteristics to generate corresponding rules to determine whether the test sample contains human faces;
③Sample learning method
This method adopts the method of artificial neural network 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 relatively concentrated distribution of facial skin color in the color space for detection.
⑤Characteristic face method
This method regards all the surface image sets as a surface image subspace, and judges whether there is a surface image based on the distance between the test sample and its projection in the subspace.
It is worth mentioning that the above five methods can also be used comprehensively in actual detection systems.
(2) Face tracking
Face tracking refers to the dynamic target tracking of the detected face. Specifically, a model-based method or a combination of motion and model is used. In addition, using skin color model tracking is also a simple and effective method.
(3) Face comparison
Face comparison is to verify the identity of the detected face or search for a target in the face image library. This actually means that the sampled face images are compared with the stock face images in turn, and the best matching object is found. Therefore, the description of the face image determines the specific method and performance of face recognition. Two description methods are mainly used: eigenvector and facial texture template:
① Feature vector method
The method is to first determine the size, position, distance and other attributes of facial features such as eye iris, nose and mouth corners, and then calculate their geometric feature quantities, and these feature quantities form a feature vector describing the facial image.
②Face pattern template method
The method is to store a number of standard face image templates or face image organ templates in the library, and during the comparison, all pixels of the sampled face image are matched with all the templates in the library using a normalized correlation measure. In addition, there are methods that use pattern recognition to combine autocorrelation networks or features and templates.
The core of face recognition technology is actually "partial human body feature analysis" and "graphic/neural recognition algorithm." This algorithm is a method that uses various organs and characteristic parts of the human face. For example, the identification parameters formed by multiple data corresponding to geometric relationships are compared, judged and confirmed with all the original parameters in the database. Generally, the judgment time is less than 1 second.
Generally divided into three steps:
(1) First create a face profile file of the human face. That is, the camera is used to collect the face image files of unit personnel's faces or take their photos to form face image files, and these face image files are generated into faceprint codes and stored.
(2) Obtain the current human face image. That is, use the camera to capture the face image of the current person entering and exit, or take a photo input, and generate the face texture code from the current face image file.
(3) Use the current facial texture code to compare with the file inventory. That is to search and compare the current facial texture code with the facial texture code in the file inventory. The above-mentioned "face texture coding" method works according to the essential features and the beginning of the human face. This facial texture coding can resist changes in light, skin tone, facial hair, hairstyle, glasses, expressions and posture, and has strong reliability, so that it can accurately identify a person from millions of people. The face recognition process can be completed automatically, continuously and in real time using ordinary image processing equipment.
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