# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS Based on Spatiotemporal Information  
The information generated by facial movements is also captured within consecutive multiple frames of a video stream. This temporal data embedded within successive frames is conducive to face detection. The flowchart for this method is depicted in Figure 8. It should be noted that the spatiotemporal information derived from facial movements still belongs to passive, one-way detection methods. In this process, the system passively receives video or image streams and then employs models such as CNNs or LSTMs to learn temporal patterns for detecting spoofs.  
[PERSON] et al. [100] utilized planar homography to detect the correlated motion patterns in replayed videos that were absent in real faces. By dividing adjacent frames into regions and calculating homography relationships, they identified spoofed faces. [PERSON] et al. [101] proposed a FAS method considering global temporal and local spatial cues combined CNN-LSTM for global spatiotemporal feature fusion and a region attention module for identifying critical local regions, thereby enhancing robustness and interpretability. [PERSON] et al. [102] proposed a method that integrated spatial gradient and temporal depth learning, employing a residual spatial gradient block (RSGB) for extracting single-frame spatial features and a cascade of short-term spatiotemporal blocks followed by ConvGRU for multi-frame depth map generation. [PERSON] et al. [103] presented a DRL-FAS framework. This method used ResNet18 for global spatial features and utilized RNN for local temporal learning, followed by feature fusion for classification. [PERSON] et al. [104] introduced a  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**Main Ideas**} & \multirow{2}{*}{**Supervision**} & \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Attasks**} & \multirow{2}{*}{**SD-M**} & \multirow{2}{*}{**OA**} & \multicolumn{2}{c}{**Advantages**} & \multicolumn{2}{c}{**Dosednessness**} \\ \cline{1-1} \cline{6-10}  & & & & & & & & & & & \\ \hline \multirow{5}{*}{[10]} & History mask and deep pixel-wise & Binary mask label & Display-Attack & \(\surd\) & \(\surd\) & & & Binary mask labels are trained to learn temporal labels for temporal and temporal and temporal and temporal and temporal and temporal \\  & & & & & & & &  
temporal transformation network (TTN) for learning temporal cues, leveraged temporal difference attention (TDA) for utilize their complementarity on multiple frames and used temporal depth difference loss (TDL) for extracting depth information. [PERSON] et al. [105] fused motion trajectories features, history cues and texture difference features from facial key points, using attention and skip connections to enhance robustness under varying lighting and attack conditions. In summary, spatiotemporal-based FAS methods effectively combine global and local features to distinguish genuine faces from spoofed ones. Nevertheless, their reliance on multi-frame processing increases computational complexity, thereby affecting the real-time performance. Table 7 provides an overview of these methods. As indicated in Table 7, this type of algorithm is capable of detecting common 2D print, 2D replay, and low-quality 3D mask attacks. However, due to the high computational overhead of multi-frame processing, it is not suitable for low-power real-time systems.