Convolutional Neural Network Models for Facial Expression Recognition Using BU-3DFE Database

Huynh, Xuan-Phung; Tran, Tien-Duc; Kim, Yong-Guk

doi:10.1007/978-981-10-0557-2_44

Cited by 23 publications

(11 citation statements)

References 8 publications

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“…We note that despite the elaborate framework proposed in [38], our less complex pipeline based on modality fusion at latent representation level significantly outperforms their reported result; see Table 2. Furthermore, we observe that [39] reported a test accuracy of 92% based on the combination of 2D and 3D features trained on DCNNs from scratch. However, [39] did not follow any of the well known protocols for training and evaluation on the BU-3DFE dataset; that is, using a set of randomly selected 60 subjects that are either fixed or not in a 10-fold cross validation scheme for training and evaluation.…”

Section: Resultsmentioning

confidence: 89%

“…Furthermore, we observe that [39] reported a test accuracy of 92% based on the combination of 2D and 3D features trained on DCNNs from scratch. However, [39] did not follow any of the well known protocols for training and evaluation on the BU-3DFE dataset; that is, using a set of randomly selected 60 subjects that are either fixed or not in a 10-fold cross validation scheme for training and evaluation. Instead, they consider the whole 100 subjects for training and evaluation, using 90 subjects for training and the remaining 10 subjects for testing.…”

Section: Resultsmentioning

confidence: 89%

“…Instead, they consider the whole 100 subjects for training and evaluation, using 90 subjects for training and the remaining 10 subjects for testing. In addition, [39] failed to report that they carried out cross validation in their experimental setup; this naturally raises two concerns: (1) a larger dataset has been used for training the proposed model (2) sampling bias for the training and testing data in view of the reported result. However, using the fusion approach that we propose in this paper and the well established training and evaluation protocol for the BU-3DFE dataset (i.e.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations

Oyedotun

Demisse

Shabayek

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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Humans use facial expressions successfully for conveying their emotional states. However, replicating such success in the human-computer interaction domain is an active research problem. In this paper, we propose deep convolutional neural network (DCNN) for joint learning of robust facial expression features from fused RGB and depth map latent representations. We posit that learning jointly from both modalities result in a more robust classifier for facial expression recognition (FER) as opposed to learning from either of the modalities independently. Particularly, we construct a learning pipeline that allows us to learn several hierarchical levels of feature representations and then perform the fusion of RGB and depth map latent representations for joint learning of facial expressions. Our experimental results on the BU-3DFE dataset validate the proposed fusion approach, as a model learned from the joint modalities outperforms models learned from either of the modalities.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

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Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations

Oyedotun

Demisse

Shabayek

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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“…Features are usually calculated on the region surrounding principal facial landmarks or on the mouth and eyes that inherently contain essential information for emotion recognition. These key features that are considered closely related to expression categories, in order to perform FER are fed to various classifiers, as well as Support-Vector Machines (SVM) [47][48][49][50][51], Adaboost, k-Nearest Neighbors (k-NN), Linear Discriminant Analysis (LDA), Modified Principal Component Analysis (PCA), Hidden Markov Model (HMM) [44][45][46], Random Forest [52] or Neural Networks [51,53,54].…”

Section: Feature-based Vs Model-based Algorithmsmentioning

confidence: 99%

3D Approaches and Challenges in Facial Expression Recognition Algorithms—A Literature Review

et al. 2019

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In recent years, facial expression analysis and recognition (FER) have emerged as an active research topic with applications in several different areas, including the human-computer interaction domain. Solutions based on 2D models are not entirely satisfactory for real-world applications, as they present some problems of pose variations and illumination related to the nature of the data. Thanks to technological development, 3D facial data, both still images and video sequences, have become increasingly used to improve the accuracy of FER systems. Despite the advance in 3D algorithms, these solutions still have some drawbacks that make pure three-dimensional techniques convenient only for a set of specific applications; a viable solution to overcome such limitations is adopting a multimodal 2D+3D analysis. In this paper, we analyze the limits and strengths of traditional and deep-learning FER techniques, intending to provide the research community an overview of the results obtained looking to the next future. Furthermore, we describe in detail the most used databases to address the problem of facial expressions and emotions, highlighting the results obtained by the various authors. The different techniques used are compared, and some conclusions are drawn concerning the best recognition rates achieved.

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“…3D FER has become an extensive field of research with many early attempts in [3], [4], [5], [6], [7] and most recent works in [8], [9], [10] that trend to use both 2D and 3D multi-modal data to further improve the accuracy. Huynh et al [11] proposed to use deep CNNs for classifying the six basic facial expressions. Two CNNs are trained on the BU-3DFE database based on the 2D facial appearance and the the 3D face shape, respectively.…”

Section: Related Workmentioning

confidence: 99%

Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition

Jan

Ding

Meng

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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Meaningful facial parts can convey key cues for both facial action unit detection and expression prediction. Textured 3D face scan can provide both detailed 3D geometric shape and 2D texture appearance cues of the face which are beneficial for Facial Expression Recognition (FER). However, accurate facial parts extraction as well as their fusion are challenging tasks. In this paper, a novel system for 3D FER is designed based on accurate facial parts extraction and deep feature fusion of facial parts. In particular, each textured 3D face scan is firstly represented as a 2D texture map and a depth map with one-to-one dense correspondence. Then, the facial parts of both texture map and depth map are extracted using a novel 4-stage process consists of facial landmark localization, facial rotation correction, facial resizing, facial parts bounding box extraction and post-processing procedures. Finally, deep fusion Convolutional Neural Networks (CNNs) features of all facial parts are learned from both texture maps and depth maps, respectively and nonlinear SVMs are used for expression prediction. Experiments are conducted on the BU-3DFE database, demonstrating the effectiveness of combing different facial parts, texture and depth cues and reporting the state-of-the-art results in comparison with all existing methods under the same setting.

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Convolutional Neural Network Models for Facial Expression Recognition Using BU-3DFE Database

Cited by 23 publications

References 8 publications

Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations

Facial Expression Recognition via Joint Deep Learning of RGB-Depth Map Latent Representations

3D Approaches and Challenges in Facial Expression Recognition Algorithms—A Literature Review

Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition

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