An End-to-End Deep Model With Discriminative Facial Features for Facial Expression Recognition

Abstract

Due to the complex challenges of the environment and emotion expressions, most facial expression recognition systems cannot achieve a high recognition rate. More discriminative features can describe facial expressions more accurately, so facial feature extraction is the key technology for facial expression recognition. In this article, an effective end-to-end deep model is proposed to improve the accuracy of face recognition. Considering the importance of data pre-processing (very few studies have focused on this process), first, a data enhancement method is proposed to locate the range of the face target and enhance the image contrast. Next, to obtain further discriminative features, a hybrid feature representation method is proposed, in which four typical feature extraction method are combined. After that, an effective deep model is designed to train and test the samples which can obtain the optimal parameters with less computation cost. Ablation study results show that the proposed hybrid feature representation method can help improve recognition accuracy. Finally, to comprehensively evaluate the performance of the proposed model, a series of experiments are conducted on three benchmark datasets. The recognition rate is achieved 94.5%, 98.6%, and 97.2% for FER2013, AR dataset, and CK+ dataset, respectively.

DLSANet: Facial expression recognition with double‐code LBP‐layer spatial‐attention network

“…The quality of facial expression features directly affects facial expression recognition accuracy. Therefore, it is imperative to extract accurate facial features to express facial expressions, which is also the focus of facial expression recognition research [22]. Feature extraction algorithms are mainly divided into geometric feature extraction and statistical feature extraction.…”

Towards a Better Performance in Facial Expression Recognition: A Data‐Centric Approach

“…After analyzing the related literature, we can say the techniques frequently used under this approach include: image preprocessing, removing noise, deleting images with errors, data augmentation, and reclassifcation. For instance, Liu et al [11] analyzed expression recognition considering the importance of data preprocessing by improving the image contrast. More discriminative facial features are obtained using a hybrid method for extraction, and a classifcation network combining EGG-16 and ResNet.…”

“…In recent years, automatic facial expression recognition (FER) has become an important area of research and development to improve human-machine interaction (HMI), leading communication to a more emotional, afective, and intelligent level [6,7]. Tis can be applied to many activities and felds such as human behavior, healthcare, medicine, psychology, psychiatry, marketing, digital advertisement, customer feedback assessment, video games, video security, video surveillance, mobile phone unlocking, crime investigation (lie detection), online learning, and automobile safety [8][9][10][11].…”

Laplacian Nonlinear Logistic Stepwise and Gravitational Deep Neural Classification for Facial Expression Recognition

“…A comprehensive survey of machine and deep learning for facial expression recognition was investigated in [12]. Yet another method employing hybrid feature representation for minimizing computation cost was proposed in [13]. With this hybrid form of feature representation recognition rate was said to be improved.…”

“…For that edge strength is evaluated. ] * cos 𝜃 𝑀𝑒𝑎𝑛𝑁𝑒𝑖𝑔ℎ (13) From the above equations ( 12) and ( 13), the edge strength '𝐸𝑆' of the corresponding features selected is estimated on the basis of the mean pixel '𝑀𝑒𝑎𝑛' value and its neighboring '𝑁𝑒𝑖𝑔ℎ' pixel value with '𝐺' representing the gravitational constant '𝐺 = 6.67259 * 10 −11 ', ' 𝐷𝑖𝑠(𝑀𝑒𝑎𝑛, 𝑁𝑒𝑖𝑔ℎ) ' denoting the distance between the mean and neighboring pixels respectively. The second hidden layer function is mathematically stated as given below.…”