  Facial expressions are one of the most effective ways for non-verbal
communications, which can be expressed as the Micro-Expression (ME) in the
high-stake situations. The MEs are involuntary, rapid, and, subtle, and they
can reveal real human intentions. However, their feature extraction is very
challenging due to their low intensity and very short duration. Although Local
Binary Pattern from Three Orthogonal Plane (LBP-TOP) feature extractor is
useful for the ME analysis, it does not consider essential information. To
address this problem, we propose a new feature extractor called Local Binary
Pattern from Six Intersection Planes (LBP-SIPl). This method extracts LBP code
on six intersection planes, and then it combines them. Results show that the
proposed method has superior performance in apex frame spotting automatically
in comparison with the relevant methods on the CASME database. Simulation
results show that, using the proposed method, the apex frame has been spotted
in 43% of subjects in the CASME database, automatically. Also, the mean
absolute error of 1.76 is achieved, using our novel proposed method.
