This paper presents two methods for the first Micro-Expression Spotting Challenge 2019 by evaluating local temporal pattern (LTP) and local binary pattern (LBP) on two most recent databases, i. e. SAMM and CAS(ME)(2). First we propose LTP-ML method as the baseline results for the challenge and then we compare the results with the LBP-chi(2) distance method. The LTP patterns are extracted by applying PCA in a temporal window on several facial local regions. The micro-expression sequences are then spotted by a local classification of LTP and a global fusion. The LBP-chi(2)-distance method is to compare the feature difference by calculating chi(2) distance of LBP in a time window, the facial movements are then detected with a threshold. The performance is evaluated by Leave-One-Subject-Out cross validation. The overlap frames are used to determine the True Positives and the metric F1-score is used to compare the spotting performance of the databases. The F1-score of LTP-ML result for SAMM and CAS(ME)(2) are 0.0316 and 0.0179, respectively. The results show our proposed LTP-ML method outperformed LBP-chi(2)-distance method in terms of F1-score on both databases.