Use of learning methods for gender and age classification based on front shot face images

Facial system estimation is a mature and in-depth research technique in age and gender. Estimation accuracy is an important indicator for evaluating algorithms. By using deep learning-based learning (DL) and machine learning, this work provides a robust approach to estimating the type and age of different external environment changes based on two different algorithms, comparing the results, and analyzing the performance of the two algorithms. The algorithm was evaluated using a data set that is considered the basis in this area of the face estimation system, namely (IMDB-WIKI) an image. The basis of the work depends on the external appearance and the front section. The results obtained: DL(Effacint-B۳) AGE Accuracy=۰.۹۹ Gender Accuracy=۰.۹۷ ML(SVM) AGE Accuracy=۰.۸۷ Gender Accuracy=۰.۹۷.

Keywords:

Face System , estimation , Age , Gender , Deep learning , Efficient-B۳ , IMDB-WIKI