Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque; Javaid, Ahmad Y.

doi:10.3390/s19081897

Cited by 60 publications

(31 citation statements)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Over the last two decades, special attention has been paid to the automatic recognition of emotions through the extracted areas of the human face. However, there is a growing demand for biometric systems and human-machine interaction where facial recognition and emotional intensity play a vital role [100].…”

Section: Discussionmentioning

confidence: 99%

A Case Study of Facial Emotion Classification Using Affdex

Magdin

Benko

Koprda

2019

Sensors

View full text Add to dashboard Cite

This paper focuses on the analysis of reactions captured by the face analysis system. The experiment was conducted on a sample of 50 university students. Each student was shown 100 random images and the student´s reaction to every image was recorded. The recorded reactions were subsequently compared to the reaction of the image that was expected. The results of the experiment have shown several imperfections of the face analysis system. The system has difficulties classifying expressions and cannot detect and identify inner emotions that a person may experience when shown the image. Face analysis systems can only detect emotions that are expressed externally on a face by physiological changes in certain parts of the face.

show abstract

Section: Discussionmentioning

confidence: 99%

A Case Study of Facial Emotion Classification Using Affdex

Magdin

Benko

Koprda

2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Mehta et al [37] gave a comparative analysis about the intensities of emotions. They use different types of hand-crafted features (Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP)).…”

Section: Related Workmentioning

confidence: 99%

eXnet: An Efficient Approach for Emotion Recognition in the Wild

Riaz

Shen

Sohail

et al. 2020

Sensors

View full text Add to dashboard Cite

Facial expression recognition has been well studied for its great importance in the areas of human–computer interaction and social sciences. With the evolution of deep learning, there have been significant advances in this area that also surpass human-level accuracy. Although these methods have achieved good accuracy, they are still suffering from two constraints (high computational power and memory), which are incredibly critical for small hardware-constrained devices. To alleviate this issue, we propose a new Convolutional Neural Network (CNN) architecture eXnet (Expression Net) based on parallel feature extraction which surpasses current methods in accuracy and contains a much smaller number of parameters (eXnet: 4.57 million, VGG19: 14.72 million), making it more efficient and lightweight for real-time systems. Several modern data augmentation techniques are applied for generalization of eXnet; these techniques improve the accuracy of the network by overcoming the problem of overfitting while containing the same size. We provide an extensive evaluation of our network against key methods on Facial Expression Recognition 2013 (FER-2013), Extended Cohn-Kanade Dataset (CK+), and Real-world Affective Faces Database (RAF-DB) benchmark datasets. We also perform ablation evaluation to show the importance of different components of our architecture. To evaluate the efficiency of eXnet on embedded systems, we deploy it on Raspberry Pi 4B. All these evaluations show the superiority of eXnet for emotion recognition in the wild in terms of accuracy, the number of parameters, and size on disk.

show abstract

“…Jeong et al [27] proposed deep joint spatiotemporal features for facial expression recognition based on the deep appearance and geometric neural networks. Mehta et al [28] recognized emotions based on its intensities while Jala and Tariq [8] aimed to get beyond classification and recognition known FE to cluster unknown facial behaviors.…”

Section: Facial Expression Recognition Applicationsmentioning

confidence: 99%

“…For instance, in [15,16] investigated the utilization of the selected features and landmarks for face recognition purposes only. Although the accuracy was the highest when both slopes and distances were used in [12], this study will use distances only as it analyzes which muscles and facial features are affected by FE, not for recognition purposes [21][22][23][24][25][26][27][28][29][30][31][32][33], evaluated the performance of FE classifications. While utilizing the periocular as a biometric trait in [33] has its failures when the face presents posture changes, occlusions, closed eyes, and other changes, in the FB, the recognition process can use other features than the one that exposes failure.…”

Section: The Effect Of Facial Expression On Face Biometric Reliabilitymentioning

confidence: 99%

The Effects of Facial Expressions on Face Biometric System’s Reliability

Alrubaish

Zagrouba

2020

Information

View full text Add to dashboard Cite

The human mood has a temporary effect on the face shape due to the movement of its muscles. Happiness, sadness, fear, anger, and other emotional conditions may affect the face biometric system’s reliability. Most of the current studies on facial expressions are concerned about the accuracy of classifying the subjects based on their expressions. This study investigated the effect of facial expressions on the reliability of a face biometric system to find out which facial expression puts the biometric system at greater risk. Moreover, it identified a set of facial features that have the lowest facial deformation caused by facial expressions to be generalized during the recognition process, regardless of which facial expression is presented. In order to achieve the goal of this study, an analysis of 22 facial features between the normal face and six universal facial expressions is obtained. The results show that the face biometric systems are affected by facial expressions where the disgust expression achieved the most dissimilar score, while the sad expression achieved the lowest dissimilar score. Additionally, the study identified the five and top ten facial features that have the lowest facial deformations on the face shape in all facial expressions. Besides that, the relativity score showed less variances between the sample using the top facial features. The obtained results of this study minimized the false rejection rate in the face biometric system and subsequently the ability to raise the system’s acceptance threshold to maximize the intrusion detection rate without affecting the user convenience.

show abstract

Recognition of Emotion Intensities Using Machine Learning Algorithms: A Comparative Study

Cited by 60 publications

References 81 publications

A Case Study of Facial Emotion Classification Using Affdex

A Case Study of Facial Emotion Classification Using Affdex

eXnet: An Efficient Approach for Emotion Recognition in the Wild

The Effects of Facial Expressions on Face Biometric System’s Reliability

Contact Info

Product

Resources

About