Smile recognition plays a vital role in human-human and human-computer interactions. This paper demonstrates a system to recognize the genuine and posed smiles by sensing observers’ galvanic skin response (GSR), while watching sets of images and videos. The smiles were shown either in ‘paired’ or in ‘single’ forms. Here, ‘paired’ means that the same smiler was seen in both genuine and posed

smile forms, otherwise the condition is referred to as ‘single’. The GSR signals were recorded and processed, and several time-domain and frequency-domain features were extracted from the processed GSR signals. Classification accuracies were found to be as high as 93.6% and 91.4% from paired and single conditions respectively. In comparison, observers were verbally 59.8% and 56.2% correct. Our results demonstrate that human subconscious responses (i.e. GSR signals) is better than their own verbal response, where the paired condition is slightly better than the single condition.