Human Age Predication from Face Images Based on Combining Deep Wavelet Network and Machine Learning Algorithms

Due to the numerous variances in face appearance, age estimation using facial images is a difficult subject. Many factors can affect the estimation of human age such as race, face post, gender, lifestyle, etc. By considering more factors, the optimum performance may be obtained. In this study, we proposed a method to predict the age of facial images. The proposed method consists of four main stages: (1) Preprocessing. (2) Face alignment and cropping. (3) Feature extraction by using Deep Wavelet Network (DWN). (4) Age prediction. Five of the machine learning classifiers (K-nearest neighbor, support vector machine, Naïve Bayes, decision tree, and random forest) were suggested in this proposal to combine with DWN and then select the best performance one. Two DWNs trained for male and female faces separately, so we have to classify faces before inputting to one of the two networks (classifying faces' gender is out of this study's scope). The performance of predicting the age was measured first when the age was divided into eleven age groups, where the accuracy was 97% for the females and 98% for the males. Also, we secondly measured the performance when the age was divided into seventeen age groups (five years for each group) with an accuracy of 91% for female and 92% for male faces.