Implementation of Gabor Filters Combined with Binary Features for Gender Recognition

Abstrak

The human face is an important biometric Includes a great deal of useful information, such as gender, age, race and identity.Gender classification is very convenient for humans,but for a computer this is a challenge. Recently, gender classification from face images is of great interest.Gender detection can be useful for human-computer interaction, Such as the designation of individuals.Several algorithms have been designed for this purpose and the proportion of each of these issues has been resolved, our proposed method is based on Gabor filters and Local Binary Patterns (LBP), which extract facial features that these characteristics are robust against interference. In order to achieve an appropriate classification, we used self-organizing neural networks, in this neural network weights are extracted for each gender with little error.The results are compared with existing data sets that this comparison will prove the superiority of the proposed method.DOI:http://dx.doi.org/10.11591/ijece.v4i1.4348