Smile is one of the representative emotional expressions which is observed frequently in daily life and essential for various non-verbal communications. People make spontaneous smiles and intentional ones. It is important to guess properly whether a person is making a smile spontaneously or intentionally to understand the meaning of smiles. In this study, we propose a smile classification system with smart eyewear that equips photo-reflective sensors and examines whether we can distinguish two types of smiles; spontaneous smiles caused by funny videos and posed smiles evoked by instructions. We extract geometric features: reflection intensity distribution of sensors and temporal features in a time axis. By applying for Support Vector Machine, we observed 94.6% as the mean accuracy among 12 participants when we used both geometric and temporal features with user-dependent training. The result suggested that we can distinguish between spontaneous and posed smile by the sensors embedded with the smart eyewear.