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The principle of face recognition technology 1, face recognition technology consists of three parts:
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 The reference template method firstly designs one or several standard face templates, then calculates the matching degree between the samples collected by the test and the standard templates, and judges whether there are human faces through the thresholds;
2 face rule method Because the face has a certain structural distribution characteristics, the so-called face rule method is to extract these features to generate the corresponding rules to determine whether the test sample contains the face;
3 Sample learning method This method uses the artificial neural network method of pattern recognition, that is, the classifier is generated by learning the face image set and the non-face sample set.
4 skin color model method This method is based on the appearance of skin color in the color space in the relative concentration of the law to detect.
2. Analysis of application prospects of face recognition technology.
5 Feature sub-face method This method treats all face image collections as a face sub-space and determines whether there is a face image based on the distance between the detected sample and its projection in the sub-space.
It is worth mentioning that the above five methods can also be comprehensively adopted in practical detection systems.
(2) Face tracking 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) Comparison of face to face comparison is to confirm the identity of the detected face or perform target search 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:
â‘ eigenvector method This method is to determine the iris of the eye, nose, mouth and other surfaces like facial features of size, position, distance and other attributes, and then calculate their geometric feature amount, the feature amounts forming a description of the face image Feature vector.
2 face pattern template 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, all the pixels of the sampled surface are used to measure the normalized correlation quantities of all the templates in the library. match. 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.
3. Analysis of application prospects of face recognition technology
Biometrics technology is widely used in government, banking, social welfare protection, e-commerce, and security defense. For example, a depositor walks into the bank. He has neither a bank card nor a withdrawal password. When he withdraws money from a cash machine, a camera scans the user's eyes and then quickly and accurately. Completed user identification and completed business. This is a real shot in a sales office at United Bank of Texas. What this department of sales uses is the "iris recognition system" in modern biometric technology. In addition, after the “9.11†incident in the United States, anti-terrorism activities have become the consensus of governments of all countries. It is very important to strengthen the security and defense of airports. Visage's face recognition technology has made great achievements at two airports in the United States. It can pick out a certain face from crowded people and judge whether he is a wanted man.
As the technology matures further and social recognition increases, face recognition technology will be applied in more fields.
1, business, residential security and management. Such as face recognition access control time and attendance systems, face recognition security doors and so on.
2, e-passport and ID card. This may be the next largest application, ICAO (ICAO) have been identified, from 2010 onwards, its 118 member countries and regions, must be machine-readable passports, face recognition technology is the most important recognition mode, this provision has been Become an international standard. One of China’s e-passport programs is stepping up its planning and implementation.
3, *, justice and criminal investigation. The use of face recognition systems and networks to search for fugitives across the country.
4, self-service. For example, if the bank's ATM is stolen, the user's card and password will be stolen by others. If face recognition is applied at the same time, this will be avoided.
5, information security. Such as computer login, e-government and e-commerce. In e-commerce transactions are all completed online, and many of the approval processes in e-government have also moved online. At present, the authorization for transactions or approvals is based on passwords. If passwords are stolen, security cannot be guaranteed. However, the use of biometrics can make the parties' digital identities and real identities on the Internet consistent, which greatly increases the reliability of e-commerce and e-government systems.
Face recognition, a biometric recognition technology based on human facial feature information. In recent years, face recognition has quickly 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 begun to enter the practical stage.