| Autor: |
Siregar, B., Setiawan, I., Efendi, S., Susilo, J. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2023, Vol. 2623 Issue 1, p1-9, 9p |
| Abstrakt: |
The human face plays an important role in our social interactions in recognizing the identity of the individual. The human face has been used as a safety lock which is part of the biometric technique. Face detection was conducted in this research using webcam-based OpenCV to assess the presence of an individual in a room. A person's presence is determined by putting a name tag on each item caught by the webcam. A face without a name tag is the face of a person who has not been registered in the system or an anonymous person, and the party in charge will take no action. The Eigenface approach is used in this method to introduce face recognition. The theory section is a calculation technique that reduces the number of variables that can be associated with a variable to a smaller number of unrelated variables. PCA is appropriate for facial recognition since it recognizes a wide range of indistinct human expressions. The basic theory of facial recognition is to quote the specific face information, then encrypt it and compare it to the previously decoded result. Decoding is accomplished using the eigenface method by calculating the eigenvector, which is then expressed in a large matrix. The eigen value of the test face image is compared and the image in the database to get an eigenvector. The closest value is used as a reference to recognize the human face that is the object of the test. The success of the test results reaches an average value of 95. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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