Human Gender and Age Detection Based on Attributes of Face


  • Shaimaa Hameed Shaker Department of Computer sciences, University of Technology in Iraq, Baghdad
  • Farah Q. Al-Khalidi Department of Computer sciences, Mustansiriyah University in Iraq, Baghdad



Facial image, Features extraction, Human Age and Gender, k-mean, LDA, ID3.


The main target of the work in this paper is to detect the gender and oldness of a person with an accurate decision and efficient time based on the number of facial outward attributes extracted using Linear-Discriminate Analysis to classify a person within a certain category according to his(her) gender and age. This work was deal with color facial images via the Iterative Dichotomiser3 algorithm as a classifier to detect the oldness of a person after gender detected. This paper used the Face-Gesture-Recognition-Research-Network aging dataset. All facial images in the dataset were categorizing into binary categories using k-means. This is followed by the process of dividing all samples according to age classes that belonging to each specific sex category. Thus, this division process enabled us to reach a quick and accurate decision.  The results showed that the accuracy of the proposal was 90.93%,  and F-measure was 89.4.




How to Cite

Hameed Shaker, S. . ., & Al-Khalidi , F. Q. . (2022). Human Gender and Age Detection Based on Attributes of Face. International Journal of Interactive Mobile Technologies (iJIM), 16(10), pp. 176–190.