Aim: The main goal of this study is to propose a novel identifying facial micro-expression using support vector machine algorithm with hidden markov model algorithm and compare their precision. Materials and Methods: The sample size for support vector machine (N=10) and hidden markov model (N=10) were iterated 20 times to detect micro expressions with g power as 80 %, threshold 1.0 and confidence interval as 95%. Results: support vector machine has significantly better precision (94.8%) compared hidden markov model precision (90.0%). The statistical significance difference p=1.0 (p<0.05) independent sample T-test value state that the results in the study are insignificant. Conclusion: Support Vector Machine algorithm offer better precision to detect facial micro-expressions than hidden markov models.