Facial expression recognition using deep learning

Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

“…For performance evaluation, we have compared our two proposed approaches with the state-of-the-art methods that used the KDEF database earlier in Table 4 : (1)–(2) a deep convolutional neural network AlexNet which is pre-trained before taken, and classification is completed with the proposed two feature selection schemes to choose either the selection of facial action units by training with binary action unit detectors for every feature map and sort them [ 49 ] or detecting the feature maps in the areas inside the face areas found by the deconvolutional neural network [ 49 ] and this selection of feature maps are influencing the classification robustness, but both these schemes achieved accuracy which is 10.2–12% less than that of our first proposed approach, and 10.9–12.7% less than our second proposed approach; (3) a multi-view facial expression is recognized by multi-channel pose aware convolutional neural network [ 50 ] and has achieved accuracy which is 11.5–12.2% less than that of our proposed approaches; (4) a CNN [ 51 ] which is pre-trained with deep stacked convolutional auto encoder (DSCAE) which will generate a feature vector for expression recognition by overcoming the illumination problem and has achieved better accuracy compared to the other five state-of-the-art methods but still 2.9–3.6% less than that of our proposed approaches; (5) adding the gradient and laplacian inputs to an image given to CNN [ 52 ] helps in recognizing the facial expression but with accuracy which is 10.2–10.9% less than that of our proposed first and second approaches; (6) a usage of the Haar classifier before feeding into the deep neural network can reduce convergence time more than others without having it and it achieved the best accuracy compared to the other state-of-the-art methods but still 1.8–2.5% less than that of our first and second proposed approaches [ 53 ]; (7) a radial basis function neural network [ 54 ] which uses a feature integration of shape descriptors and texture features for expression recognition has achieved accuracy which is 9.6–10.3% less than that of our first and second proposed approaches.…”

“…P. Ramakrishna et al [ 52 ] proposed a real-time neural network with a spatial transformal layer and laplacian operators and have achieved 88.1% accuracy on the KDEF dataset. S. Hari et al [ 53 ] developed a deep learning based F.E.R. system and achieved an accuracy of 96.6% on the KDEF dataset.…”

A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach

“…Our proposed hybrid approach is compared with recent state-of-the-art FER methods, including MULTICNN [ 28 ], HDNN [ 38 ], RTCNN [ 29 ], ALEXNET+LDA [ 41 ], MSLBP+SVM [ 23 ], DL-FER [ 34 ], and RBFNN [ 26 ], which used either machine learning methods alone without using CNN or a combined model of CNN and SVM methods on the KDEF dataset. Table 5 reports our experimental results and shows the comparison with these methods.…”

“…Table 5 reports our experimental results and shows the comparison with these methods. In Table 5 , MSLBP+SVM [ 23 ], RBFNN [ 26 ] used machine learning methods for feature extraction and classification, whereas DL-FER [ 34 ], MULTICNN [ 28 ] and RTCNN [ 29 ] used deep neural networks alone. ALEXNET+LDA [ 41 ] and HDNN [ 38 ] used the framework of combining deep neural network for feature extraction and machine learning techniques for classification.…”

“…S.H. Abdullahi et al, 2021 [ 34 ] proposed a deep-learning-based FER system with a label-smoothening mechanism, achieving an accuracy of 96.6% on the KDEF dataset.…”