# CFormerFaceNet: Efficient Lightweight Network Merging a CNN and Transformer for Face Recognition
## 4 Experiments
### Training Data and Test Data  
The cleaned MS1M [44] dataset is used as the training dataset and contains 5.1 M face images of 93 K identities from different races, ages, and genders around the world. The LFW [45], CALFW [46], CPLFW [47], SLLFW [48], CFP [49], and AgeDB-30 [50] datasets are employed for testing.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Layer** & **Output Size** & **\#Layers (n)** & **Kernel** & **Output Channels** \\ \hline Image & \(112\times 112\) & 1 & - & - \\ \hline Downsampling & \(56\times 56\) & 1 & \(2\times 2\) & 32 \\ Conv. Block & \(56\times 56\) & 3 & \(3\times 3\) & 32 \\ \hline Downsampling & \(28\times 28\) & 1 & \(2\times 2\) & 64 \\ Conv. Block & \(28\times 28\) & 5 & \(5\times 5\) & 64 \\ STDA Block & \(28\times 28\) & 1 & - & 64 \\ \hline Downsampling & \(14\times 14\) & 1 & \(2\times 2\) & 96 \\ Conv. Block & \(14\times 14\) & 5 & \(7\times 7\) & 96 \\ STDA Block & \(14\times 14\) & 1 & - & 96 \\ \hline Downsampling & \(7\times 7\) & 1 & \(2\times 2\) & 128 \\ Conv. Block & \(7\times 7\) & 2 & \(9\times 9\) & 128 \\ STDA Block & \(7\times 7\) & 1 & - & 128 \\ \hline Conv2d & \(7\times 7\) & 1 & \(1\times 1\) & 512 \\ Global Depth-wise conv. & \(1\times 1\) & 1 & \(7\times 7\) & 512 \\ Linear & \(1\times 1\) & 1 & - & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the model’s layers with respect to the output size, kernel size, and output channels, repeated n times.  
LFW (Labeled Faces in the Wild) is a dataset comprising 13,233 face images obtained from the internet, with 5749 identities that have been labeled. The images exhibit diversity in terms of ethnicity, gender, age, expression, pose, and lighting conditions and have become the standard benchmark for assessing the efficacy of face recognition algorithms.  
The CALFW (Cross-Age LFW), CPLFW (Cross-Pose LFW), and SLLFW (Similar-Looking LFW) datasets are extensions of the widely used LFW dataset and were designed to emphasize the challenges of recognizing faces with age differences, faces with pose variations, and similar-looking faces, respectively. These datasets were constructed to provide more realistic and challenging benchmarks for evaluating the effectiveness of face recognition algorithms in real-world scenarios.  
The CFP (Celebrities in Frontal-Profile) dataset was created to evaluate the accuracy of face recognition algorithms in recognizing faces with frontal-profile pose variations. It includes 500 identities, each with 10 frontal face images and 4 profile face images. Similar to the LFW dataset, the dataset is divided into 10 subsets, each containing 350 pairs of same-class samples and 350 pairs of different-class samples. The CFP dataset serves as a benchmark for evaluating the performance of face recognition algorithms in recognizing faces with frontal-profile pose variations. The CFP dataset has two settings for evaluation: frontal-to-frontal verification (CFP_FF) and frontal-to-profile verification (CFP_FP).  
The AgeDB-30 dataset comprises 12,240 face images of 440 individuals from various professions. Each image is labeled with the subject's identity, age, and gender. The dataset is divided into 4 testing tasks, each consisting of 10 subsets. Each subset includes 300 pairs of same-class samples and 300 pairs of different-class samples. The age difference between sample pairs varies across the four testing tasks and is set to 5 years, 10 years, 20 years, and 30 years, respectively. The AgeDB-30 dataset serves as a benchmark for evaluating the performance of cross-age face recognition algorithms.