Driver’s Facial Expression Recognition in Real-Time for Safe Driving

Jeong, Mira; Ko, Byoung Chul

doi:10.3390/s18124270

Cited by 135 publications

(80 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several researchers have studied mainly to recognize patterns using images [13], video [14], speech [15], text [16], and other datasets [17]. It is also applied to other problems such as recognizing the emotions of people [18,19] and predicting power consumption [20]. Deep learning, which has become a huge tide in the field of big data and artificial intelligence, has made a significant breakthrough in machine learning and pattern recognition research.…”

Section: Introductionmentioning

confidence: 99%

Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning

Kim

Cho

2019

Mathematics

View full text Add to dashboard Cite

Peer-to-Peer (P2P) lending transactions take place by the lenders choosing a borrower and lending money. It is important to predict whether a borrower can repay because the lenders must bear the credit risk when the borrower defaults, but it is difficult to design feature extractors with very complex information about borrowers and loan products. In this paper, we present an architecture of deep convolutional neural network (CNN) for predicting the repayment in P2P social lending to extract features automatically and improve the performance. CNN is a deep learning model for classifying complex data, which extracts discriminative features automatically by convolution operation on lending data. We classify the borrower’s loan status by capturing the robust features and learning the patterns. Experimental results with 5-fold cross-validation show that our method automatically extracts complex features and is effective in repayment prediction on Lending Club data. In comparison with other machine learning methods, the standard CNN has achieved the highest performance with 75.86%. Exploiting various CNN models such as Inception, ResNet, and Inception-ResNet results in the state-of-the-art performance of 77.78%. We also demonstrate that the features extracted by our model are better performed by projecting the samples into the feature space.

show abstract

Section: Introductionmentioning

confidence: 99%

Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning

Kim

Cho

2019

Mathematics

View full text Add to dashboard Cite

show abstract

“…[31]: The expanded Cohn-Kanade (CK+) dataset is a public benchmark dataset for facial expression recognition (FER) and has 327 image sequences from 118 subjects and seven facial expression labels based on FACS. The feature vector consists of 84 dimensional distance ratios and 88 dimensional angles that are extracted from the facial landmarks [32]. MNIST dataset [33]: The Modified National Institute of Standards and Technology (MNIST) dataset contains images of handwritten digits and is also widely used for an evaluation in the field of machine learning.…”

Section: Methodsmentioning

confidence: 99%

Interpretation and Simplification of Deep Forest

Kim¹,

Jeong²,

Ko³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

<div><br></div><div>This paper proposes a new method for interpreting and simplifying a black box model of a deep random forest (RF) using a proposed rule elimination. In deep RF, a large number of decision trees are connected to multiple layers, thereby making an analysis difficult. It has a high performance similar to that of a deep neural network (DNN), but achieves a better generalizability. Therefore, in this study, we consider quantifying the feature contributions and frequency of the fully trained deep RF in the form of a decision rule set. The feature contributions provide a basis for determining how features affect the decision process in a rule set. Model simplification is achieved by eliminating unnecessary rules by measuring the feature contributions. Consequently, the simplified model has fewer parameters and rules than before. Experiment results have shown that a feature contribution analysis allows a black box model to be decomposed for quantitatively interpreting a rule set. The proposed method was successfully applied to various deep RF models and benchmark datasets while maintaining a robust performance despite the elimination of a large number of rules.<br></div>

show abstract

“…The network has potential applications in tracking, image retrieval, and facial emotion recognition. Compared to deep learning based methods, Jeong et al [25] proposed a simple machine learning based facial emotion recognition method. Acharya et al [26] argued that regional distortion of facial images is useful for facial expressions.…”

Section: Visual Signal Based Emotion Classificationmentioning

confidence: 99%

Facial Emotion Recognition Using Hybrid Features

Alreshidi

Ullah

2020

Informatics

View full text Add to dashboard Cite

Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ recognition. The framework consists of two machine learning algorithms (for detection and classification) that could be trained offline for real-time applications. Initially, we detect faces in the images by exploring the AdaBoost cascade classifiers. We then extract neighborhood difference features (NDF), which represent the features of a face based on localized appearance information. The NDF models different patterns based on the relationships between neighboring regions themselves instead of considering only intensity information. The study is focused on the seven most important facial expressions that are extensively used in day-to-day life. However, due to the modular design of the framework, it can be extended to classify N number of facial expressions. For facial expression classification, we train a random forest classifier with a latent emotional state that takes care of the mis-/false detection. Additionally, the proposed method is independent of gender and facial skin color for emotion recognition. Moreover, due to the intrinsic design of NDF, the proposed method is illumination and orientation invariant. We evaluate our method on different benchmark datasets and compare it with five reference methods. In terms of accuracy, the proposed method gives 13% and 24% better results than the reference methods on the static facial expressions in the wild (SFEW) and real-world affective faces (RAF) datasets, respectively.

show abstract

Driver’s Facial Expression Recognition in Real-Time for Safe Driving

Cited by 135 publications

References 43 publications

Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning

Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning

Interpretation and Simplification of Deep Forest

Facial Emotion Recognition Using Hybrid Features

Contact Info

Product

Resources

About