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Face recognition, a biometric recognition technology based on human facial feature information. In recent years, face recognition has rapidly become a hot spot in the global market in recent years, as face recognition technology in developed countries in Europe and the United States has entered the practical stage. Face recognition technology often listens, but do you know how it is implemented?
Face recognition technology consists of three parts:
Face detection
Face detection refers to judging the presence or absence of a face image in a dynamic scene and a complex background, and separating the face image. There are generally the following methods:
1 reference template method
First, a template of one or several standard faces is designed, and then the degree of matching between the sample collected by the test and the standard template is calculated, and a threshold is used to determine whether there is a face.
2 face rule method
Because the face has certain structural distribution characteristics, the so-called face rule method is to extract these features and generate corresponding rules to judge whether the test sample contains a face.
3 sample learning
This method uses the method of artificial neural network in pattern recognition, that is, the classifier is generated by learning the face sample set and the non-face sample set.
4 skin color model
This method is based on the rule that the appearance of skin color is relatively concentrated in the color space.
5 feature sub-face method
This method treats all face image collections as a facet subspace and determines whether there is a face 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 comprehensively adopted in practical detection systems.
2. Face tracking
Face tracking refers to the dynamic target tracking of the detected face. Specifically, a model-based approach or a combination of motion and model approach is used. In addition, using skin color model tracking is also a simple and effective means.
3. Face matching
The face comparison is to confirm the identity of the detected face or search for the target in the face library. This actually means that the sampled face image is compared with the face image of the stock in order and the best match 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: feature vector and texture template:
1 Feature Vector Method
The method is to determine the attributes such as the size, position, and distance of facial contours such as the iris, nose, mouth, etc., and then calculate their geometric features. These feature quantities form a feature vector describing the facial image.
2 face pattern method
The method is to store a plurality of standard face image templates or face image organ templates in the library. When comparing, all the pixels of the sampled surface are matched with all the templates in the library by a normalized correlation measure. In addition, there is a method of using autocorrelation of pattern recognition or a combination of features and templates.
The core reality of face recognition technology is “local human character analysis†and “graphic/neural recognition algorithm.†This algorithm is a method that uses various organs and feature parts of the human face. For example, the corresponding data of multiple geometric identifications and the original parameters in the database are compared, judged and confirmed. The general requirement is to judge that the time is less than 1 second.
The original title face recognition technology often listens, but do you know how it is implemented?