## 4 Discussion  
The state-of-the-art on the Adience database is listed in Table 3. The facial image alignment was studied to improve the classification accuracies along with the effects of dropout by using the SVM based classifier [57]. The work in [5] was the first step in age and gender classification from face images by using DNNs. It employed a relatively simple and shallow network for feature extraction and classification stages and it proposed an over-sampling technique to solve the misalignment challenge partially. [PERSON] and [PERSON] proposed a compact DCNN architecture for age and gender estimation [58]. The aim of their work is to train a fast DCNN as a base for the DNN in video training with the input image size of 32 \(\times\) 32. [PERSON] et al. studied a DCNN for a multitask learning to train the shared features for age and gender tasks in end-to-end manner [59]. The works in [5; 58; 59] built simple CNNs without utilizing any pre-trained models. Based on their reported results, one can notice that the methods are not efficient for extracting age-related features, since the models were trained by using the Adience database, which is considered to be a relatively small database. A cascaded CNN is introduced in age estimation in [35]. It consists of three modules: age group classifier, DCNN bases regressors, and erroneous age prediction. [PERSON] et al. utilized a deep CNN architecture that is based on the VGG-16 model that was pre-trained for image classification. It does not rely on facial landmarks to extract facial age-related features. The pretrained DNN was used as a facial feature extractor [60]. The models are mainly built and trained for apparent age estimation. Since apparent age estimation and real age estimation are different tasks, the reported improvements are limited when compared with other works [35; 60]. A new face descriptor model is presented in [61] that is based on three attributes: (1) The age primitives, which finds the crucial texture primitives; (2) The latent second direction to keep the structural information; and, (3) The global adaptive threshold to discriminate in the flat and textured region. The new descriptor was used to extract facial features for age classification. In work [62], the label-sensitive deep metric learning (LSDML) is proposed to learn a discriminative feature similarity in facial age estimation. The goal was to exploit the label correlation between the training face samples in term of the labels (subspace) to achieve balanced training samples. In [63], a new hybrid architecture is developed based on CNN and extreme learning machine (ELM). Both techniques combined together to deal with age and gender classification. The CNN is used for feature extraction, while the ELM is used as a classifier. Different techniques for facial age estimation are proposed in [60; 61; 62; 63]. A new DCNN model that was based on an attention network is introduced in [64]. This model estimates the most informative patches in low-resolution images, which are further processed in a patch network in higher resolution. Figure 8: Challenging images in the Adience database. Images in the top row were classified incorrectly by the proposed networks. Images in the bottom row were classified correctly by the proposed work. In Table 3, the proposed work outperforms the state-of-the-art methods because the proposed method extracts more effective age-related feature sets and uses more capable classifier architectures, while all of the previous works focused on only one of these tasks. Previous works, which did not use DNNs or used a shallow DNN that was trained on one relatively small database, did not achieve satisfactory results [5; 35; 57; 59]. Previous works that used relatively deep NNs trained on several age databases with different augmentations to increase the training samples achieved slightly improved results, such as the work in [60], subject-exclusive DAPP in [61], LSDML and M-LSDML in [62], and [63]. The highest accuracy reported on the Adience database is 62.2%.