# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM  
[PERSON]  
1 The Department of Automotive Lighting Convergence Engineering, Yeungnam University,  
Gyeongsan 38541, Korea; [EMAIL_ADDRESS] (J-C.K. ); [EMAIL_ADDRESS] (M-H.K. ); [EMAIL_ADDRESS] (H-E.S.) [PERSON]  
1 The Department of Automotive Lighting Convergence Engineering, Yeungnam University,  
Gyeongsan 38541, Korea; [EMAIL_ADDRESS] (J-C.K. ); [EMAIL_ADDRESS] (M-H.K. ); [EMAIL_ADDRESS] (H-E.S.) [PERSON]  
1 The Department of Automotive Lighting Convergence Engineering, Yeungnam University,  
Gyeongsan 38541, Korea; [EMAIL_ADDRESS] (J-C.K. ); [EMAIL_ADDRESS] (M-H.K. ); [EMAIL_ADDRESS] (H-E.S.) [PERSON]  
2 Research Institute of Human Ecology, Yeungnam University,  
Gyeongsan 38541, Korea; [EMAIL_ADDRESS] (H-.) [PERSON]  
1 The Department of Automotive Lighting Convergence Engineering, Yeungnam University,  
Gyeongsan 38541, Korea; [EMAIL_ADDRESS] (H-E.S.) Footnote 1: email: [EMAIL_ADDRESS]. prehensive literature review on this subject can be found in review papers [12; 13; 14]. These approaches can be categorized as model-based, image-based and deep learning-based. Most of the image-based approaches are based on engineered features such as Histogram of Oriented Gradients (HOG) [15], Local Binary Pattern Histogram (LBPH) [16] and Gabor filters [17]. Facial landmark points are one of the key features of model-based facial expression analysis. Statistical shape models such as active shape models (ASMs) [8] or appearance models such as active appearance models (AAM) [9] are frequently used with classifiers such as a support vector machine (SVM). A facial expression recognition system using multi-class AdaBoost [18] with pairs of landmarks exhibits high performance in facial expression recognition [19]. Automatic landmark point detection and tracking using multiple differential evolution Markov chain particle filters [20] exhibits improved performance over conventional landmark point tracking based on AAM [9]. An SVM classifier on the displacement of facial landmark points yields high classification accuracy [21]. Recently, convolutional neural networks (CNNs) have been used for face recognition [22; 23] and facial expression recognition [24; 25; 26]. The face alignment and normalization process using 3D face modeling shows an improvement in face recognition performance in CNN-based face recognition [22]. CNN architectures specialized for facial action unit recognition for supporting region-based multi-label learning have also been developed [27]. Deep CNN provides high-level features from the trained deep model. Features extracted from the CNN are very powerful and are used for numerous computer vision applications [28], including facial expression recognition using SVM [24; 29; 30] and micro-expression recognition tasks with evolutionary search for features [31]. A hybrid model that integrates CNN and SVM by replacing the fully connected classification layer of the CNN with an SVM classifier has also been developed [32]. Efforts have been made to combine multiple approaches for the improved classification of facial expression. Two-channel CNNs with different kernels are combined in a fully connected layer and exhibit better performance than hand-coded features [33]. The ensemble of multiple deep CNNs is used for static facial expression recognition [34]. Deep belief networks (DBN) are used to extract and combine audio and visual features for emotion recognition from audio-video data [35]. Especially for the recognition of facial expressions, combining geometric features and texture features is important for achieving improved performance by compensating for the limitations of one of the features: the geometry provides global facial motions, whereas the texture provides subtle and detailed variations in expression, such as a wink or eyebrow movement.