Facial expression recognition using ensemble of classifiers

Zavaschi, Thiago H. H.; Koerich, Alessandro L.; Oliveira, Luiz S.

doi:10.1109/icassp.2011.5946775

Cited by 27 publications

(6 citation statements)

References 14 publications

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“…Over the past decade, facial expression recognition (ER) has been a topic of significant interest. Many ER techniques have been proposed to automatically detect the seven universally recognizable types of emotions -joy, surprise, anger, fear, disgust, sadness and neutral -from a single still facial image [6], [14], [18], [23], [26], [27]. These static techniques for facial ER tend to follow either appearance-based or geometric-based approaches.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

Ortega,

Senoussaoui,

Granger

et al. 2019

Preprint

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This paper presents a novel deep neural network (DNN) for multimodal fusion of audio, video and text modalities for emotion recognition. The proposed DNN architecture has independent and shared layers which aim to learn the representation for each modality, as well as the best combined representation to achieve the best prediction. Experimental results on the AVEC Sentiment Analysis in the Wild dataset indicate that the proposed DNN can achieve a higher level of Concordance Correlation Coefficient (CCC) than other stateof-the-art systems that perform early fusion of modalities at feature-level (i.e., concatenation) and late fusion at scorelevel (i.e., weighted average) fusion. The proposed DNN has achieved CCCs of 0.606, 0.534, and 0.170 on the development partition of the dataset for predicting arousal, valence and liking, respectively.

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Section: Related Workmentioning

confidence: 99%

Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

Ortega,

Senoussaoui,

Granger

et al. 2019

Preprint

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“…Local Binary Patterns (LBP) has been recently proposed as effective appearance descriptors for facial analysis [10,11]. It achieved a convincing performance compared with Gabor filters but with a light computational cost [12,13]. A variation of LBP has been proposed by Rajamanoharan et al [14] for 3D facial action unit detection.…”

Section: Introductionmentioning

confidence: 99%

Combining appearance and geometric features for facial expression recognition

Liu

2015

SPIE Proceedings

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This paper introduces a method for facial expression recognition combining appearance and geometric facial features. The proposed framework consistently combines multiple facial representations at both global and local levels. First, covariance descriptors are computed to represent regional features combining various feature information with a low dimensionality. Then geometric features are detected to provide a general facial movement description of the facial expression. These appearance and geometric features are combined to form a vector representation of the facial expression. The proposed method is tested on the CK+ database and shows encouraging performance.

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“…Automatic facial expression recognition has been investigated in the last years due to the great number of applications, ranging from human-computer interaction, emotion analysis [2] to detection of driver fatigue [24]. Several approaches based on handcrafted features [3], [14], [24], [25] such as textural, eigenfaces, etc. have been shown to be very effective.…”

Section: Introductionmentioning

confidence: 99%

Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition

Tannugi

Britto

Koerich

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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Facial expression recognition is a major problem in the domain of artificial intelligence. One of the best ways to solve this problem is the use of convolutional neural networks (CNNs). However, a large amount of data is required to train properly these networks but most of the datasets available for facial expression recognition are relatively small. A common way to circumvent the lack of data is to use CNNs trained on large datasets of different domains and fine-tuning the layers of such networks to the target domain. However, the fine-tuning process does not preserve the memory integrity as CNNs have the tendency to forget patterns they have learned. In this paper, we evaluate different strategies of fine-tuning a CNN with the aim of assessing the memory integrity of such strategies in a cross-dataset scenario. A CNN pre-trained on a source dataset is used as the baseline and four adaptation strategies have been evaluated: fine-tuning its fully connected layers; fine-tuning its last convolutional layer and its fully connected layers; retraining the CNN on a target dataset; and the fusion of the source and target datasets and retraining the CNN. Experimental results on four datasets have shown that the fusion of the source and the target datasets provides the best trade-off between accuracy and memory integrity.

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Facial expression recognition using ensemble of classifiers

Cited by 27 publications

References 14 publications

Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

Combining appearance and geometric features for facial expression recognition

Memory Integrity of CNNs for Cross-Dataset Facial Expression Recognition

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