Emotion recognition is a process by which human emotional states can be identified. Most of the present methods make use of visual and audio information’s together. With recent advancements in deep neural networking, there are several methodologies to identify human emotional states. One of the methods that detect the emotional states is based on a multimodal Deep Convolution Neural Network (DCNN), that use both the audio and visual cues in a deep model. BLSTM-RNN is another method which makes use of multimodal features to capture emotions. A much more efficient approach is using a convolutional neural network (CNN) to extract features from the speech, and for the visual modality, the features can be extracted using a deep residual network of 50 layers. To capture contextual information’s a long short-term memory network can be utilized above these two models. Deep belief networks are another method which takes multimodal emotion recognition into account by first learning the features of the audio and video separately; after which it concatenates these two features. Visual features hold more importance in emotion recognition, so ResNet along with SVR for training can be used to predict emotion states effectively.

DCNN, DBN, Residual Network, LSTM, SVR

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