# Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
## 4 Experiments  
### Implementation Details  
**Datasets.** Several public cross-age datasets were used for model training and evaluation: cross-age celebrity dataset (CACD) [20], CACD verification subset (CACD-VS) [20], AgeDB [41], CALFW (cross-age labeled face in the wild) [42], and face and gesture recognition network (FG-NET) [43] dataset. We used some of the CACD dataset to train our model, and the rest of them were used for evaluation. The distribution of ages in the CACD and FG-NET datasets is shown in Figure 6. The CACD dataset is used as a public benchmark for CAFR, and it is composed of 163,446 images of 2000 celebrities. The images reflect various shooting conditions, such as illumination variations, pose variations, age variations, makeup, and practical scenarios. FG-NET contains 1002 images of 82 people with an age range from 0 to 69; FG-NET has larger age gaps than CACD, but it contains only a few images of a small number of people. The CACD dataset can effectively reflect the robustness of our CAFR algorithm.  
Figure 6: The distribution of ages in two of the datasets.  
Therefore, we choose the CACD dataset as the training dataset. We randomly selected 80% of its images as the training data (130,757 images) and used the remaining 20% for validation (32,689 images). However, the CACD dataset contains some incorrectly labeled samples and duplicate images. In particular, the age labels do not match the real age. We used the DEX [44] method to produce age labels as the ground truth. To obtain better training results, we also manually removed duplicate images.  
We conducted experiments on commonly used, public, cross-age datasets: CACD-VS, CALFW, AgeDB, and FG-NET. We extracted only the identity-specific feature as the final face feature representation for identity recognition in the testing process. The cosine similarity of these representations was then used to conduct face verification and identification.  
**Data preprocessing.** The CACD dataset was used to fine-tune our network in the experiments. We used the multi-task cascaded convolutional network [45] to detect face areas and facial landmarks in the training images. After detecting the eye position, we applied an affine transformation to the data to align the face images based on the detected eye coordinates. All faces were globally cropped to 112 \(\times\) 112 based on five facial landmarks (two mouth corners, nose center, and two eyes) and a similarity transformation. Figure 7 shows some original and preprocessed face images from the CACD dataset.  
**Training protocols.** As the CNN architecture of the ResNet module has been proved to be an effective mapping function, we used IResNet-101, which is pre-trained on the general face dataset MS-Celeb-1M, as the backbone to capture the most prominent features for identity discrimination. MS-Celeb-1M is a dataset that contains 5.8 million images of 8500 subjects across pose and age. The pre-trained model can classify tens of thousands of identities and extract multilevel, high-resolution features.  
We initialized the shared model with the pre-trained model and then trained the feature to decompose the module on the CACD dataset with a batch size of 512 on four Nvidia Titan X Pascal GPUs. The models were trained with the SGD algorithm and a momentum of 0.9 and weight decay of \(5\times 10^{-4}\). We selected the hyperparameters by trial and error. The training process was finished at 30 epochs of 9.57 K iterations. We used Adam as an optimizer and set the initial learning rate to 0.01. We followed the common setting as given in [10] to set the scale factor and multiplicative margin of CurricularFace loss to 64 and 0.5, respectively. Figure 8 shows the variation trend for training loss with the optimal parameters.  
Figure 7: Examples of the data used. The top row shows the original images, and the bottom row shows the aligned and normalized images.