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Emotion recognition using EEG signals has become a hot research topic in the last few years. This paper aims at providing a novel method for emotion recognition using less channels of frontal EEG signals. By employing the asymmetry theory of frontal brain, a new method fusing spatial and frequency features was presented, which only adopted two channels of frontal EEG signals at Fp1 and Fp2. In order to estimate the efficiency of the method, a GBDT classifier was evaluated and selected, and the method was implemented on the DEAP database. The maximum and mean classification accuracy were achieved as 76.34% and 75.18% respectively, which exhibited the best result comparing with other related studies. This method is extremely suitable for wearable EEG monitoring applications in human daily life.