Fuzzy and entropy facial recognition
This is an incremental improvement in facial recognition technology, potentially benefiting security and identification systems.
The paper tackles facial recognition by proposing a method that combines fuzzy theory and Shannon entropy to simplify the process and improve learning performance, claiming it requires only two data points per learning and achieves high accuracy using stable facial factors.
This paper suggests an effective method for facial recognition using fuzzy theory and Shannon entropy. Combination of fuzzy theory and Shannon entropy eliminates the complication of other methods. Shannon entropy calculates the ratio of an element between faces, and fuzzy theory calculates the member ship of the entropy with 1. More details will be mentioned in Section 3. The learning performance is better than others as it is very simple, and only need two data per learning. By using factors that don't usually change during the life, the method will have a high accuracy.