# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS with RGB Camera
#### 3.1.1 FAS Combining Handcraft and DL
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Main Idea** & **Ref.** & **Attacks** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Methods based on motion} & Gaze tracking & [33; 34] & & Taking into account motion characteristics, & \\  & Eye blinking & [35; 36] & & They can effectively resist & It is difficult to deal with replay videos \\  & Nodding and Smiling & [37] & & they can effectively resist & attacks, longer detection time. \\  & Lip reading & [38] & & print attacks. & \\ \hline \multirow{4}{*}{Methods based on life information} & rPPG & [39; 40; 41] & 3 DM & High accuracy under & Video input is required but lacks \\  & Optical flow & [42; 43] & 2D-P, 2D-R & specific constraints. & robustness when highly affected by \\  & & Micro-movements & [44; 45] & 2D-P, 3 DM & strong generalization for & individual movements. \\ \hline \multirow{4}{*}{Methods based on texture texture} & LBP-TOP & [46; 47] & & With a small amount of & \\  & Color texture & [48] & & calculation and a & It is easily affected by recording \\  & SIFT & [49] & & relatively stable & \\  & SURF & [50] & environment, it can & image quality, and its generalization \\  & HOG & [51] & & effectively resist print & capability across datasets is not strong. \\  & DoG & [52] & & and replay attacks. & \\ \hline \multirow{4}{*}{Methods based on image quality} & Image specular reflection, color distribution, and sharpness & [26; 53] & \multirow{4}{*}{2D-P} & Relatively strong & \\  & & & & generalization ability for & \\ \cline{1-1}  & Infrared image & [54; 55] & & single-type spoofed & resist high-resolution matte photo and \\ \cline{1-1}  & Light field & [56; 57] & & processing speed. & \\ \hline \multirow{4}{*}{Methods based on hardware} & Infrared image & [54; 55] & \multirow{4}{*}{2D-P, 2D-R} & \multirow{4}{*}{High accuracy} & It requires the addition of expensive \\ \cline{1-1}  & Light field & [56; 57] & & & hardware devices, and the time of \\ \cline{1-1}  & Depth map & [58] & & & processing images increases. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Methods based on traditional handcrafted features. For simplicity, 2D-P represents print attacks, 2D-R represents replay attacks, 3D-M represents 3D masks attacks, OA represents obfuscation attacks, and Adv represents physical adversarial attacks. All subsequent tables are similar. for extracting features, applied principal component analysis (PCA) for dimensionality reduction, and employed Support Vector Machine (SVM) for classification. [PERSON] et al. [64] cascaded LBP-TOP with CNN to capture spatio-temporal features via histograms from CNN layers, and classified by SVM with nonlinear RBF kernel. [PERSON] et al. [65] extracted features with CNN, used optical flow for facial motion detection, and jointly learned spatial and channel features. [PERSON] et al. [66] utilized the power of CNN filters and texture encoding for silicone mask attacks. [PERSON] et al. [67] integrated face image quality assessment (FIQA) with neural network decomposition for improving generalization. Feature-level fusion: [PERSON] et al. [68] proposed a dual-stream approach combining LBP and simplified weber local descriptor (SWLD) encoded CNN. The fused features ensured the preservation of the local intensity information and the orientations of the edges. [PERSON] et al. [69] developed a method using rPPG signals and vision transformer (ViT) to detect 3D mask attacks by analyzing the physiological differences between real faces and spoofed masks. [PERSON] et al. [70] addressed replay video attacks via motion blur analysis, extracting temporal blur descriptors with a 1D CNN and blur width using local similarity pattern (LSP) features, which were fused for detection. [PERSON] et al. [71] introduced a method combining deep features extracted by CNN and color texture features by using rotation-invariant local binary pattern (RI-LBP), applying the fused data to an SVM for classification. Decision-level fusion: [PERSON] et al.