[Retracted] Facial Expression Recognition Based on Convolutional Neural Network Fusion SIFT Features of Mobile Virtual Reality

Abstract

Facial expression recognition computer technology can obtain the emotional information of the person through the expression of the person to judge the state and intention of the person. The article proposes a hybrid model that combines a convolutional neural network (CNN) and dense SIFT features. This model is used for facial expression recognition. First, the article builds a CNN model and learns the local features of the eyes, eyebrows, and mouth. Then, the article features are sent to the support vector machine (SVM) multiclassifier to obtain the posterior probabilities of various features. Finally, the output result of the model is decided and fused to obtain the final recognition result. The experimental results show that the improved convolutional neural network structure ER2013 and CK+ data sets’ facial expression recognition rate increases by 0.06% and 2.25%, respectively.

Database Development and Recognition of Facial Expression using Deep Learning

“…To detect facial expressions, the proposed model uses a convolution neural network. Convolution Neural network (CNN) [11,16,17,37,45,47] The pooling layer is a key pillar of CNN. It reduces the dimensionality of the feature vector.…”

“…Adholiya [46]Developed a deep convolution neural network model using tf.Keras. Yao and Qiu[47] Propose hybrid features that combine convolutional neural networks and dense SIFT features for low-pixel facial images. This model uses local features like eyes, eyebrows, and mouth to detect facial expressions.Zhang et al[18] proposes a method built on a convolution neural network as an image edge identi cation to detect facial expressions.From the literature, it is found that the retrieval of facial features, like geometry and texture features, is done using a variety of traditional approaches.Appearance-based feature extraction and feature-based feature extraction are the two categories of feature extraction methodologies.…”

Prediction of Diseases Based on Facial Diagnosis Using Deep Learning

“…Mohamad Al Jazaery et.al. [16] Instead of employing C2D features separately, C3D features were used to simultaneously describe spatial and temporal salient featuresTrials using the AVEC2013 and AVEC2014 datasets have demonstrated the viability of the visual-based method.Future evaluations of the RNN-C3D can focus on additional issues related to human behaviour comprehension Anping Song et al [17] The Inception-v3 architecture serves as the foundation for the entire model. IDFNP concatenated the parameters of the two portions using a concat layer in addition to the core elements of Inception-v3 and DeepID.Reached a level of accuracy that is on par with that of neurologists.…”

Retracted: Facial Expression Recognition Based on Convolutional Neural Network Fusion SIFT Features of Mobile Virtual Reality

“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”