Real time automated facial expression recognition app development on smart phones

Alshamsi, Humaid; Këpuska, Veton; Meng, Hongying

doi:10.1109/iemcon.2017.8117150

Cited by 24 publications

(12 citation statements)

References 39 publications

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“…So, accuracy reached up to 97% but in the case of spontaneous data (data similar to the real-life situation) like FER2013 researcher got a maximum of 75-76% accuracy. Table 2 summarize the accuracy difference between posed [15,29,31,[40][41][42][43][44] and spontaneous datasets [17,19,30,[45][46][47]. Posed datasets always get greater accuracy than spontaneous but are less reliable in real-life applications.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Facial Emotion Intensity: A Fusion Way

Pandey

Kumar

2022

SN COMPUT. SCI.

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Many scientific works have been conducted for developing the Emotion intensity recognition system. But developing a system that is capable to estimate small to peak intensity levels with less complexity is still challenging. Therefore, we propose an effective facial emotion intensity classifier by fusion of the pre-trained deep architecture and fuzzy inference system. The pre-trained architecture VGG16 is used for basic emotion classification and it predicts emotion class with the class index value. By class index value, images are sent to the corresponding Fuzzy inference system for estimating the intensity level of detected emotion. This fusion model effectively identifies the facial emotions (happy, sad, surprise, and angry) and also predict the 13 categories of emotion intensity. This fusion model got 83% accuracy on a combined dataset (FER 2013, CK + and KDEF). The performance and findings of this proposed work are further compared with state-of-the-art models.

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Section: Discussion and Future Workmentioning

confidence: 99%

Facial Emotion Intensity: A Fusion Way

Pandey

Kumar

2022

SN COMPUT. SCI.

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“…It includes a set of 4,900 images in facial expressions from 70 subjects (35 female and 35 male), displaying seven emotions (angry, fearful, disgusted, happy, sad, surprised, and neutral) [86], using faces of subjects of age between 20-30 years. During the session, intrusive elements causing any disruption were excluded from their faces such as earrings, facial hair, moustaches, jewelry, makeup, beards, or glasses [87]. Each expression was photographed from five different angles and was recorded two times.…”

Section: Karolinska Directed Emotional Faces (Kdef)mentioning

confidence: 99%

Generative Adversarial Networks for Facial Expression Recognition in the Wild

Alharbawee,

Pugeault

2024

IJCDS

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The task of modeling and identifying people's emotions using facial cues is a complex problem in computer vision. Normally we approach these issues by identifying Action Units (AUs), which have many applications in Human Computer Interaction (HCI). Although Deep Learning approaches have demonstrated a high level of performance in recognizing AUs and emotions, they require large datasets of expert-labelled examples. In this article, we demonstrate that good deep features can be learnt in an unsupervised fashion using Deep Convolutional Generative Adversarial Networks (DCGANs), allowing for a supervised classifier to be learned from a smaller labelled dataset. The paper primarily focuses on two key aspects: firstly, the generation of facial expression images across a wide range of poses (including frontal, multi-view, and unconstrained environments), and secondly, the analysis and classification of emotion categories and Action Units. We demonstrate an enhanced ability to generalize and achieve successful results by using a different methodology and a variety of datasets for feature extraction and classification. Our method has been thoroughly tested through multiple experiments on different databases, leading to promising results.

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“…Jabid et al [22] investigated the local directional pattern (LDP), an appearance-based feature extraction method. Alshami et al [23] SVM method was used to study two feature descriptors, facial landmarks descriptor and center of gravity descriptor. SVM and numerous other methods (e.g., KNN, LDA) were taken into account in the comparative analysis conducted by Liew and Yairi [11] for the classification of features extracted using various methods, including Gabor, Haar, and LBP.…”

Section: Machine Learning-based Fer Approachmentioning

confidence: 99%

“…In addition, Porcu et al [38] used different data augmentation techniques, including synthetic images to train deep CNN architectures and combined synthetic images with other methods to perform the FER system better. Existing deep learning-based methods have also taken frontal images into account, and most studies have even excluded profile view images from the data set in experiments to simplify the task [11,23,30,39].…”

Section: Deep Learning-based Fer Approachmentioning

confidence: 99%

Transfer Learning Technique with EfficientNet for Facial Expression Recognition System

Alam¹,

Kartowisastro²,

Wicaksono³

2022

RIA

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Facial Expression Recognition (FER) systems are helpful in a wide range of industries, including healthcare, social marketing, targeted advertising, the music industry, school counseling systems, and detection in the police sector. In this research, using Deep Convolutional Neural Networks (DCNN) architecture, specifically from the EfficientNet family (EfficientNet-B0, Efficient-Net-B01, EfficientNet-B02, EfficientNet-B03, EfficientNet-B04, EfficientNet-B05, EfficientNet-B06, and EfficientNet-B07) has previously gone through a combined scaling process of combined dept, width and resolution. First, the previously frozen sublayer EfficientNet model was used for feature extraction. Next, the layer closer to the output layer is melted by several layers to be retrained in order to recognize the pattern of the CK+ and JAFFE data sets. This process is called the transfer learning technique. This technique is very powerful for working on relatively small data sets, namely CK+ and JAFFE. The main of this research is to improve the accuracy and performance of facial expression recognition models with a transfer learning approach using EfficientNet pre-trained with fine-tuning strategy. Our proposed method, specifically using the EfficientNet-B0 architecture, achieves superior performance for each of the CK+ and JAFFE datasets, achieving 99.57% and 100% accuracy in the test set respectively.

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Real time automated facial expression recognition app development on smart phones

Cited by 24 publications

References 39 publications

Facial Emotion Intensity: A Fusion Way

Facial Emotion Intensity: A Fusion Way

Generative Adversarial Networks for Facial Expression Recognition in the Wild

Transfer Learning Technique with EfficientNet for Facial Expression Recognition System

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