## 4 Experiments and Results
### Datasets  
In this paper, we use four different datasets for real age and gender estimation. We first introduce the datasets with a description of their specifications. Figure 7 represents exemplar images for each dataset used for the age estimation experiment and sample images for gender prediction are presented in Figure 8. Table 4 shows the size of each dataset with its properties. For the age classification, we trained our model with the IMDB-WIKI dataset and fine-tuned it with the MORPH dataset. We evaluate our age estimation model with the MORPH dataset. In the gender estimation task, we train and evaluate our model with the Adience dataset. IMDB-WIKI: IMDB-WIKI is the biggest face image dataset labeling for age and gender that is free for public use. This dataset contains images with real age annotation in the range 0-100 and a total of 523,051 celebrity face images. The images were crawled from the IMDb website and Wikipedia. The age of a person is calculated based on the date of birth and the timestamp when the photo was taken (crawled from the sources). Among the half-a-million images, around 460 k images of 20,284 subjects are collected from IMDb and the rest, 62 k images are crawled straightaway from Wikipedia. A lot of images of this dataset are substandard images for training, such as humorous images, sketch images, severely occluded, full-length images, images containing multiple subjects, and blank images. For our experiment, firstly, we consider single-person images and then remove the wrongly annotated faces from individual classes. Thenceforth, we perform class-wise down-sampling until a threshold is basically formed by considering the low sample classes existing in the dataset. Finally, we enhance the image of the lower sampled classes from other similar datasets and perform data augmentation to make a nearly balanced dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & \begin{tabular}{c} **Number of** \\ **Images** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Subjects** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Images** \\ \end{tabular} & \begin{tabular}{c} **Testing/** \\ **Validation Images** \\ \end{tabular} & \
\begin{tabular}{c} **Experiment** \\ **Age Range** \\ \end{tabular} \\ \hline IMDB & 460,723 & 20,284 & 85,532 & 21,400 &
\begin{tabular}{c} Real Age \\ Estimation \\ Real Age \\ Estimation \\ Real Age \\ Estimation \\ Age Range/ \\ Estimation \\ Age Range/ \\ Estimation \\ \end{tabular} & 0–100 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The particulars of the datasets used for our experiments with the corresponding training and testing split. Figure 8: Exemplar images from the Adience dataset used for the gender estimation experiment. Figure 7: Exemplar images with real (biological) age from each dataset used in the age estimation experiment. dataset. As a result, the total number of images that belong to the balanced dataset is 107 k and we use approximately 85 k images for training which is 80% of the balanced IMDB-WIKI dataset. MORPH: The Craniofacial Longitudinal Morphological Face Database (Morph) is the most used dataset for real age estimation. It is a publicly available facial aging benchmark with about 55,000 facial images from more than 13,000 subjects. MORPH comprises 46,645 images of males and 8487 images of females with an age range from 16 to 77 years. For our age estimation experiments, we adopt the setup often used in the literature [22, 24, 30, 48, 49, 52], where a subset of Caucasian people's images is used for the experiment. The system is evaluated by taking 20% of the images from this subset while the remaining 80% are used for network training. Although these works [50, 77] do not follow the same setup while experimenting, we still report their result because of using the same benchmark. Adience: Adience is a collection of face images from real-world and unconstrained imaging conditions.