# Facial Expression Recognition Based on Auxiliary Models
## 2 Related Work  
A detailed overview for expression recognition was given by [PERSON] [20] and [PERSON] [21]. This section discusses some recent methods that achieve high accuracy in facial expression recognition using a comparable experimental methodology. [PERSON] et al. [20] proposed an approach called Boosted-LBP to extract the most discriminant LBP features, and the best recognition performance is obtained by using Support Vector Machine classifiers with Boosted-LBP features. They conducted experiments on the Cohn-Kanade database, MMI database and JAFFE database. They showed that the LBP-based SVMs perform slightly better than the Gabor-wavelet based SVMs by using the 10-fold cross-validation on each dataset. [PERSON] et al. [22] proposed a method which uses Haar classifier for face detection purpose and Local Binary Pattern (LBP) histograms of different block sizes of a face image as feature vectors, and classifies various facial expressions using Principal Component Analysis (PCA). They used grayscale frontal face images of a person to classify six basic emotions, namely happiness, sadness, disgust, fear, surprise and anger. [PERSON] et al. [23] proposed a novel facial expression recognition method using local binary pattern (LBP) and local phase quantization (LPQ) based on Gabor face image. Firstly, Gabor wavelets can capture the prominent visual attribute by extracting multi-scale and multi-direction spatial frequency features from the face images, which is separable and robust to illumination changes. Then, the LBP and LPQ feature based on the Gabor wavelet transform are fused for face representation. Considering the dimension of the fused feature is too large, the PCA-LDA algorithm is used to extract complex features. The method is finally tested and verified by multi-class SVM classifiers. This approach was implemented on JAFFE database. Two methods were used to test the effect of the classification. The first validation method was \"leave one out\". All expression images of one subject were selected as testing samples and the rest images as training samples, and it achieved a recognition accuracy of 81.82%. The other validation method was that two samples of each facial expression for each person were used to form the training set, and the remaining samples were used for testing. The proposed method showed the recognition rate of 98.57%. [PERSON] et al. [24] proposed a novel algorithm for Facial Expression Recognition (FER), which is based on fusion of Gabor texture features and Local Phase Quantization (LPQ). Firstly, the LPQ feature and Gabor texture feature are, respectively, extracted from ever expression image. The image is first transformed by LPQ, and then divided into 3 \(\times\) 5 blocks. Then, the LPQ histograms are calculated from each block. LPQ histograms of 15 blocks are concatenated into a long series of histogram as a single vector. Then, Five scales and eight orientations of Gabor wavelet filters are used to extract Gabor texture features and adaboost algorithm is used to select Gabor features. Gabor features are obtained by 40 filters. Then, adaboost algorithm is used to select the 100 most effective features from each Gabor features image. Finally, the final concatenates the 4000 features from the 40 Gabor features images used as facial expression features. They obtained two expression recognition results on both expression features by Sparse Representation based Classification (SRC) method. Finally, the final expression recognition is performed by fusion of residuals of two SRC algorithms. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm was better than the original two algorithms, and this algorithm had a much higher recognition rate of 73.33%. [PERSON] et al. [25] used the Convolutional neural network model to realize facial expression recognition.