3D Facial Expression Recognition Using Deep Feature Fusion CNN

Tian, Kun; Zeng, Liaoyuan; McGrath, Sean; Yin, Qian; Wang, Wenyi

doi:10.1109/issc.2019.8904930

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…We only compare with DL works which use Bosphorus dataset. The authors of [ 37 , 38 ] use the same database and type of data. Table 19 shows the comparison.…”

Section: Discussionmentioning

confidence: 99%

Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition

Perez-Gomez

Ríos-Figueroa

Rechy-Ramirez

et al. 2020

Sensors

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An essential aspect in the interaction between people and computers is the recognition of facial expressions. A key issue in this process is to select relevant features to classify facial expressions accurately. This study examines the selection of optimal geometric features to classify six basic facial expressions: happiness, sadness, surprise, fear, anger, and disgust. Inspired by the Facial Action Coding System (FACS) and the Moving Picture Experts Group 4th standard (MPEG-4), an initial set of 89 features was proposed. These features are normalized distances and angles in 2D and 3D computed from 22 facial landmarks. To select a minimum set of features with the maximum classification accuracy, two selection methods and four classifiers were tested. The first selection method, principal component analysis (PCA), obtained 39 features. The second selection method, a genetic algorithm (GA), obtained 47 features. The experiments ran on the Bosphorus and UIVBFED data sets with 86.62% and 93.92% median accuracy, respectively. Our main finding is that the reduced feature set obtained by the GA is the smallest in comparison with other methods of comparable accuracy. This has implications in reducing the time of recognition.

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“…We only compare with DL works which use Bosphorus dataset. The authors of [ 37 , 38 ] use the same database and type of data. Table 19 shows the comparison.…”

Section: Discussionmentioning

confidence: 99%

Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition

Perez-Gomez

Ríos-Figueroa

Rechy-Ramirez

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Then, a CNN model is learned in such a way that only the features from significant regions are taken into consideration. Tian et al 22 fused different features and trained a deep convolution neural network, where, in order to avoid over fitting, average pooling layer is used in lieu of fully connected layer. Zhen et al 23 presented an approach with regard to muscular movement model, by which the face is segmented into different regions.…”

Section: Related Workmentioning

confidence: 99%

3D face expression recognition with ensemble deep learning exploring congruent features among expressions

Rajagopal

Baskaran

2021

Computational Intelligence

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Automatic and accurate 3D face expression categorization has been a challenging task in many computer vision applications. This article presents a novel approach named 3D face expression recognition with ensemble deep learning (3D‐FER‐EDL). The framework comprises three levels. Each of the first two levels gives preference to a subset of labels, considering the congruent features among expressions. Each 3D expressive face is represented as a CDS features vector, which combines corner, distance, and slope features. CDS feature vector is given as input to the first level learners. Expression probability vectors from the first level learners are jointly given as the input to the second level learners. The probability vectors from level one and two are given as the input to the third level, which is the meta classifier. The experiments are conducted on the Bosphorus database and the CASIA 3D face database to evaluate the effectiveness of the proposed approach.

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“…Facial expressions are very important information in human behavior research. To recognize three-dimensional faces, Tian et al proposed a deep feature fusion convolutional neural networks (CNN) [28], which combines different two-dimensional face information to fine-tune the network model, and achieves effective detection results. In addition, Starzacher et al combined artificial neural networks and support vector machines to achieve feature fusion and applied it to embedded devices [29].…”

Section: Related Workmentioning

confidence: 99%

Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation

et al. 2020

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Feature pyramids of convolutional neural networks (ConvNets)—from bottom to top—are used by most recent researchers for the improvement of object detection accuracy, but they seldom aim to address the correlation of each feature channel and the fusion of low-level features and high-level features. In this paper, an Attention Pyramid Network (APN) is proposed, which mainly contains the adaptive transformation module and feature attention block. The adaptive transformation module utilizes the multiscale feature fusion, and makes full use of the accurate target location information of low-level features and the semantic information of high-level features. Then, the feature attention block strengthens the features of important channels and weakens the features of unimportant channels through learning. By implementing the APN in a basic Mask R-CNN system, our method achieves state-of-the-art results on the MS COCO dataset and 2018 WAD database without bells and whistles. In addition, the structure of the APN makes the network parameters lighter, and runs at 4 ms on average, which is ignorable when compared to the inference time of the backbone of ConvNet.

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3D Facial Expression Recognition Using Deep Feature Fusion CNN

Cited by 8 publications

References 20 publications

Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition

Feature Selection on 2D and 3D Geometric Features to Improve Facial Expression Recognition

3D face expression recognition with ensemble deep learning exploring congruent features among expressions

Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation

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