1 INTRODUCTION  
1.1 Challenges and differences from conventional techniques  
1.2 Contributions  

2 DIFFERENCES BETWEEN MACRO- AND MICRO-EXPRESSIONS  

3 DATASETS OF MICRO-EXPRESSIONS  

4 MICRO-EXPRESSION FEATURES  
4.1 DT features in the spatio-temporal domain  
4.2 Frequency domain features  
4.3 DT features in tensor-decomposition spaces  
4.4 Optical flow features  
4.5 Deep features  
4.6 Feature interpretability  

5 SPOTTING ALGORITHMS  
5.1 Optical flow-based spotting  
5.2 Feature descriptor based spotting  

6 RECOGNITION ALGORITHMS  
6.1 Traditional classification algorithms  
6.2 Deep learning  
6.3 Transfer learning from other domains  

7 APPLICATIONS  

8 COMPARISON  
8.1 Evaluation of spotting algorithms  
8.2 Evaluation of recognition algorithms  
8.3 Encoding micro- from macro-expressions  

9 FUTURE DIRECTIONS  

10 CONCLUSION