Facial expression recognition with local prominent directional pattern

Makhmudkhujaev, Farkhod; Abdullah-Al-Wadud, M.; Iqbal, Tauhid Bin; Ryu, Byungyong

doi:10.1016/j.image.2019.01.002

Cited by 61 publications

(16 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Facial expression recognition plays an important role in various fields in recent decades; many practical applications have been found, such as human-machine interaction, affective computing, surveillance, and robot control [21]. Most of the developed approaches focus on representing variations of appearances of different facial features (muscle motions) to interpret the range of individual expressions as one feature and look for a pattern among them to recognize the different expressions.…”

Section: Automatic Facial Expression Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Cognitive Ergonomics Evaluation Assisted by an Intelligent Emotion Recognition Technique

et al. 2020

View full text Add to dashboard Cite

The study of the cognitive effects caused by work activities are vital to ensure the well-being of a worker, and this work presents a strategy to analyze these effects while they are carrying out their activities. Our proposal is based on the implementation of pattern recognition techniques to identify emotions in facial expressions and correlate them to a proposed situation awareness model that measures the levels of comfort and mental stability of a worker and proposes corrective actions. We present the experimental results that could not be collected through traditional techniques since we carry out a continuous and uninterrupted assessment of the cognitive situation of a worker.

show abstract

Section: Automatic Facial Expression Recognitionmentioning

confidence: 99%

“…Hence, an unambiguous feature descriptor is a key component of expression recognition. However, constructing a stable descriptor, which is robust against such changes, is a challenging task [21].…”

Section: Automatic Facial Expression Recognitionmentioning

confidence: 99%

Cognitive Ergonomics Evaluation Assisted by an Intelligent Emotion Recognition Technique

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Some of the works carried out on ISED datasets have predominantly used CNN as the crux, while some modifications on the network is incorporated to enhance the accuracy of the algorithm [25][26]. Feature extraction methods involving Local prominent directional patterns and local directional structural pattern have been used.…”

Section: Related Workmentioning

confidence: 99%

“…Feature extraction methods involving Local prominent directional patterns and local directional structural pattern have been used. However, these methods lack in efficient classification accuracy when compared to the CNN's [25][26][27]. Many authors use modified CNN in order to achieve greater results by adding multiple deep layers that enhance the performance of the system [29-31].…”

Section: Related Workmentioning

confidence: 99%

Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks

Thiruthuvanathan¹,

Balachandran²,

Rangaswamy³

2021

IJIES

View full text Add to dashboard Cite

Online teaching and learning has recently turned out to be the order of the day, where majority of the learners undergo courses and trainings over the new environment. Learning through these platforms have created a requirement to understand if the learner is interested or not. Detecting engagement of the learners have sought increased attention to create learner centric models that can enhance the teaching and learning experience. The learner will over a period of time in the platform, tend to expose various emotions like engaged, bored, frustrated, confused, angry and other cues that can be classified as engaged or disengaged. This paper proposes in creating a Convolutional Neural Network (CNN) and enabling it with residual connections that can enhance the learning rate of the network and improve the classification on three Indian datasets that predominantly work on classroom engagement models. The proposed network performs well due to introduction of Residual learning that carries additional learning from the previous batch of layers into the next batch, Optimized Hyper Parametric (OHP) setting, increased dimensions of images for higher data abstraction and reduction of vanishing gradient problems resulting in managing overfitting issues. The Residual network introduced, consists of a shallow depth of 50 layers which has significantly produced an accuracy of 91.3% on ISED & iSAFE data while it achieves a 93.4% accuracy on the Daisee dataset. The average accuracy achieved by the classification network is 0.825 according to Cohens Kappa measure.

show abstract

“…[46] 92. 5 Meena et al (2020) [47] 92.9 Wei et al (2020) [39] 94.4 Makhmudkhujaev et al (2019) [48] 94.5 Cheng and Zhou (2020) [49] 96.0 Chen and Hu (2020) [50] 96.3 Gan et al (2020) [38] 96.3 De la torre et al (2015) [51] 96.4 Qin et al(2020) [40] 96.8 Dyn-HOG (with multi-class SVM) 96.8 Salmam et al (2019) [19] 96.9 Li et al (2020) [52] 97. 4 Zhao et al (2018) [32] with optical flow 97.5 FlowCorr (with mult-iclass SVM) 98.0 Sadeghi and Raie (2019) [53] 98.2 Table 10 encapsulates percentage accuracy comparison for each emotion of the presented descriptors with a perceptual study of human interpretation of basic emotions conducted by Calvo et al [9] on KDEF-dyn dataset.…”

Section: Recognition Performance Of Dyn-hog Descriptormentioning

confidence: 99%

Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions

Anthwal¹,

Ganotra²

2020

RIA

View full text Add to dashboard Cite

Facial expressions are the most preeminent means of conveying one’s emotions and play a significant role in interpersonal communication. Researchers are in pursuit of endowing machines with the ability to interpret emotions from facial expressions as that will make human-computer interaction more efficient. With the objective of effective affect cognition from visual information, we present two dynamic descriptors that can recognise seven principal emotions. The variables of the appearance-based descriptor, FlowCorr, indicate intra-class similarity and inter-class difference by quantifying the degree of correlation of optical flow associated with the image pair and each pre-designed template describing the motion pattern associated with different expressions. The second shape-based descriptor, dyn-HOG, finds the HOG values of the difference image derived by subtracting neutral face from emotional face, and is demonstrated to be more discriminative than previously used static HOG descriptors for classifying facial expressions. Recognition accuracies with multi-class support vector machine obtained on the CK+ and KDEF-dyn datasets are competent with the results of state-of-the-art techniques and empirical analysis of human cognition of emotions.

show abstract

Facial expression recognition with local prominent directional pattern

Cited by 61 publications

References 28 publications

Cognitive Ergonomics Evaluation Assisted by an Intelligent Emotion Recognition Technique

Cognitive Ergonomics Evaluation Assisted by an Intelligent Emotion Recognition Technique

Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks

Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions

Contact Info

Product

Resources

About