Human emotions are recognized using face recognition, speech recognition, physiological signals recognition etc. This paper represents Electrocardiogram (ECG) signal for emotion recognition thorough analysis of its psychological properties to recognize human emotion, it can reflect peoples true emotion and provide smooth interface between human and computer. Each signal is empirically decomposed by using Empirical Mode Decomposition (EMD) into finite set of small oscillatory activity called Intrinsic Mode Functions (IMF). The information components of interest are then combined to create feature v ector based on the combination methods for exploiting the fission - fusion processes provided by Hilbert - Huang transform. In the next stage, classification is performed by using Multi class Support Vector Machines to identify four emotional states (joy, ange r, sadness and pleasure) of human body. When we evaluated the algorithm on database recorded at university of Augsburg, the proposed method achieved improved recognition accuracy for subj ect - independent classification
