# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
Texture feature extraction based on discriminative non-negative matrix factorization (DNMF) and geometric displacement from grid tracking was used for distance measurement and fused by the SVM classifier [36]. Recently, learning shape, appearance and dynamics with a combination of a CNN and bi-directional long short-term memory neural network (BLSTM) were used for facial action unit recognition [37]. As an alternative to this approach, the joint fine-tuning of deep neural networks from a deep temporal appearance network (DTAN) and deep temporal geometry network (DTGN) is proposed [38]. Two pre-trained networks and joint loss functions are fine-tuned with additional fully connected layers. A great deal of work is done by using the hybrid approach, a combination of transfer learning and pre-trained deep convolutional networks [39]. The model classifies the facial expressions into seven classes. For the FER-2013 database, the model gives accuracy of 74.39%. Another facial expression recognition work is also presented using graph-based feature extraction and hybrid classification approach (GFE-HCA) [40]. Feature dimensions from facial parts such as the right eye, left eye, nose and mouth are optimized using a weighted visibility graph, which catalyzes the graph-based features from the edge-based invariant features. The combination of a deep convolutional network and modified joint trilateral filter is also used to efficiently recognize facial emotions [41]. However, a system that combines a conventional feature-based classifier and deep learning approaches does not exist. The method discussed in [42] addresses a unique artificial intelligence (AI)-based system for speech emotion recognition (SER) that utilizes hierarchical blocks of the convolutional long short-term memory (ConvLSTM) with sequence learning. In order to extract the local emotional features in a hierarchical correlation, four blocks of ConvLSTM are used. In addition, to extract the spatial cues by utilizing the convolution operations, the ConvLSTM layers are adopted for input-to-state and state-to-state transition. Moreover, in order to extract the global information and adaptively adjust the relevant global feature weights according to the correlation of the input features, a novel sequence learning strategy is also utilized. In this paper, we have presented the hybrid model: a fusion of CNN-based facial expression recognition and conventional high-performance geometry-based facial expression recognition using SVM classifiers. In order to extract facial motion characteristics efficiently, we applied deep learning algorithms to the dense facial motion flow, which was the extracted optical flow from the initial frame (neutral expression) to the given frame of the sequence. In the case of geometric displacement, the displacement direction and strength of the facial landmark points was extracted and used as a feature vector for the SVM classifier of the geometric facial expression data. The weighted summation of the result of the hybrid classifiers provides state-of-the art results for the Cohn-Kanade (CK+) database [43] and is satisfactory for the BU4D database, which is still comparable with the state-of-the-art schemes.