Human emotion plays an important role in our daily life. EEG-based emotion recognition is expected to facilitate our understanding of the neural mechanisms as well as applications of emotion recognition. Most EEG-based emotion recognition studies have employed the classical event-related approach, which may not be the most suitable tool for naturalistic situations with continuous audio-visual emotional information streams.In the present study, we explored emotion recognition using EEG-based inter-subject correlation (ISC) features. ISC measures the consistency of neural responses across a group of participants exposed to identical complex and continuous stimuli, characterizing the neural responses from a multi-person perspective. Using a publicly available EEG-based emotion database named DEAP, in which 32 participants watched 40 video clips with different emotional properties, ISCs over left parietal cortex and frontal region are found to be significantly correlated with arousal ratings and valence ratings, respectively (arousal: r=0.41, p=0.008; valence: r=0.37, p=0.017) . In addition, using ISCs as features, binary classification accuracies for arousal and valence reached 77.5% and 70%, which are superior to the traditional individual spectral power based method (44.1±13.9% and 48.6±12.7%). Our results suggest that the inter-subject correlation approach as an effective and promising candidate for investigating human emotion experiences.