LCAD - Contribuições do(a) usuário(a) [pt-br]

Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order

2016-07-21T18:14:03Z

André Teixeira Lopes:

[[category:Publicações]]
Journal Paper, Pattern Recognition International Journal, 2016

DOI: [http://dx.doi.org/10.1016/j.patcog.2016.07.026]

PDF: [http://www.sciencedirect.com/science/article/pii/S0031320316301753]

Accepted Manuscript: [http://www.lcad.inf.ufes.br/~alopes/papers/patcog-si-deep-image-2016/accepted-manuscript.pdf]
== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Alberto F. De Souza, Thiago Oliveira-Santos

== Abstract ==
Abstract Facial expression recognition has been an active research area in the past ten years, with growing application areas including avatar animation, neuromarketing and sociable robots. The recognition of facial expressions is not an easy problem for machine learning methods, since people can vary significantly in the way they show their expressions. Even images of the same person in the same facial expression can vary in brightness, background and pose, and these variations are emphasized if considering different subjects (because of variations in shape, ethnicity among others). Although facial expression recognition is very studied in the literature, few works perform fair evaluation avoiding mixing subjects while training and testing the proposed algorithms. Hence, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps. Convolutional Neural Networks achieve better accuracy with big data. However, there are no publicly available datasets with sufficient data for facial expression recognition with deep architectures. Therefore, to tackle the problem, we apply some pre-processing techniques to extract only expression specific features from a face image and explore the presentation order of the samples during training. The experiments employed to evaluate our technique were carried out using three largely used public databases (CK+, \{JAFFE\} and BU-3DFE). A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods −96.76% of accuracy in the CK+ database - it is fast to train, and it allows for real time facial expression recognition with standard computers.

[http://www.lcad.inf.ufes.br/~alopes/papers/patcog-si-deep-image-2016/graphical-abstract.png Graphical Abstract]

== BibTeX ==

@article{Lopes2016,
title = "Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order ",
journal = "Pattern Recognition ",
year = "2016",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/j.patcog.2016.07.026",
url = "http://www.sciencedirect.com/science/article/pii/S0031320316301753",
author = "Andre Teixeira Lopes and Edilson de Aguiar and Alberto F. De Souza and Thiago Oliveira-Santos"
}

Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order

2016-07-21T17:57:05Z

André Teixeira Lopes:

[[category:Publicações]]
Journal Paper, Pattern Recognition International Journal, 2016

DOI: [http://dx.doi.org/10.1016/j.patcog.2016.07.026]

PDF: [http://www.sciencedirect.com/science/article/pii/S0031320316301753]

== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Alberto F. De Souza, Thiago Oliveira-Santos

== Abstract ==
Abstract Facial expression recognition has been an active research area in the past ten years, with growing application areas including avatar animation, neuromarketing and sociable robots. The recognition of facial expressions is not an easy problem for machine learning methods, since people can vary significantly in the way they show their expressions. Even images of the same person in the same facial expression can vary in brightness, background and pose, and these variations are emphasized if considering different subjects (because of variations in shape, ethnicity among others). Although facial expression recognition is very studied in the literature, few works perform fair evaluation avoiding mixing subjects while training and testing the proposed algorithms. Hence, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps. Convolutional Neural Networks achieve better accuracy with big data. However, there are no publicly available datasets with sufficient data for facial expression recognition with deep architectures. Therefore, to tackle the problem, we apply some pre-processing techniques to extract only expression specific features from a face image and explore the presentation order of the samples during training. The experiments employed to evaluate our technique were carried out using three largely used public databases (CK+, \{JAFFE\} and BU-3DFE). A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods −96.76% of accuracy in the CK+ database - it is fast to train, and it allows for real time facial expression recognition with standard computers.

[http://www.lcad.inf.ufes.br/~alopes/papers/patcog-si-deep-image-2016/graphical-abstract.png Graphical Abstract]

== BibTeX ==

@article{Lopes2016,
title = "Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order ",
journal = "Pattern Recognition ",
year = "2016",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/j.patcog.2016.07.026",
url = "http://www.sciencedirect.com/science/article/pii/S0031320316301753",
author = "Andre Teixeira Lopes and Edilson de Aguiar and Alberto F. De Souza and Thiago Oliveira-Santos"
}

Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order

2016-07-21T17:56:18Z

André Teixeira Lopes: Criou página com 'category:Publicações Journal Paper, Pattern Recognition International Journal, 2016 DOI: [http://dx.doi.org/10.1016/j.patcog.2016.07.026] PDF: [http://www.sciencedirec...'

[[category:Publicações]]
Journal Paper, Pattern Recognition International Journal, 2016

DOI: [http://dx.doi.org/10.1016/j.patcog.2016.07.026]

PDF: [http://www.sciencedirect.com/science/article/pii/S0031320316301753]

== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Alberto F. De Souza, Thiago Oliveira-Santos

== Abstract ==
Abstract Facial expression recognition has been an active research area in the past ten years, with growing application areas including avatar animation, neuromarketing and sociable robots. The recognition of facial expressions is not an easy problem for machine learning methods, since people can vary significantly in the way they show their expressions. Even images of the same person in the same facial expression can vary in brightness, background and pose, and these variations are emphasized if considering different subjects (because of variations in shape, ethnicity among others). Although facial expression recognition is very studied in the literature, few works perform fair evaluation avoiding mixing subjects while training and testing the proposed algorithms. Hence, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of Convolutional Neural Network and specific image pre-processing steps. Convolutional Neural Networks achieve better accuracy with big data. However, there are no publicly available datasets with sufficient data for facial expression recognition with deep architectures. Therefore, to tackle the problem, we apply some pre-processing techniques to extract only expression specific features from a face image and explore the presentation order of the samples during training. The experiments employed to evaluate our technique were carried out using three largely used public databases (CK+, \{JAFFE\} and BU-3DFE). A study of the impact of each image pre-processing operation in the accuracy rate is presented. The proposed method: achieves competitive results when compared with other facial expression recognition methods −96.76% of accuracy in the CK+ database - it is fast to train, and it allows for real time facial expression recognition with standard computers.

[http://www.lcad.inf.ufes.br/~berriel/paper-si-auto-vision/images/graphical-abstract.png Graphical Abstract]

== BibTeX ==

@article{Lopes2016,
title = "Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order ",
journal = "Pattern Recognition ",
year = "2016",
issn = "0031-3203",
doi = "http://dx.doi.org/10.1016/j.patcog.2016.07.026",
url = "http://www.sciencedirect.com/science/article/pii/S0031320316301753",
author = "Andre Teixeira Lopes and Edilson de Aguiar and Alberto F. De Souza and Thiago Oliveira-Santos"
}

Publicações do LCAD

2016-07-21T16:23:20Z

André Teixeira Lopes: /* Papers Summaries */

[[category:Publicações]]
== [http://www.lcad.inf.ufes.br/~alberto/pesquisadores-lcad-producao Publicações do LCAD desde 1996] ==

== Papers Summaries ==

[[A Particle Filter-based Lane Marker Tracking Approach using a Cubic Spline Model]], 2015, SIBGRAPI

[[A Facial Expression Recognition System Using Convolutional Networks]], 2015, SIBGRAPI

[[Facial Expression Recognition with Convolutional Neural Networks: Coping with Few Data and the Training Sample Order]], 2016, Pattern Recognition International Journal

A Facial Expression Recognition System Using Convolutional Networks

2016-07-21T16:17:43Z

André Teixeira Lopes:

[[category:Publicações]]
Conference Paper, SIBGRAPI, 2015

DOI: [http://dx.doi.org/10.1109/SIBGRAPI.2015.14]

PDF: [http://sibgrapi.sid.inpe.br/col/sid.inpe.br/sibgrapi/2015/06.19.21.39/doc/PID3755347.pdf]

== Authors ==
Andre Teixeira Lopes, Edilson de Aguiar, Thiago Oliveira-Santos

== Abstract ==
Facial expression recognition has been an active research area in the past ten years, with a growing application area like avatar animation and neuromarketing. The recognition of facial expressions is not an easy problem for machine learning methods, since different people can vary in the way that they show their expressions. And even an image of the same person in one expression can vary in brightness, background and position. Therefore, facial expression recognition is still a challenging problem in computer vision. In this work, we propose a simple solution for facial expression recognition that uses a combination of standard methods, like Convolutional Network and specific image pre-processing steps. Convolutional networks, and the most machine learning methods, achieve better accuracy depending on a given feature set. Therefore, a study of some image pre-processing operations that extract only expression specific
features of a face image is also presented. The experiments were carried out using a largely used public database for this problem. A study of the impact of each image pre-processing operation in the accuracy rate is presented. To the best of our knowledge, our method achieves the best result in the literature, 97.81% of accuracy, and takes less time to train than state-of-the-art methods.

[[imagem:Resumo_A_Facial_Expression_Recognition_System_Using_Convolutional_Networks.jpg]]

== BibTeX ==

@INPROCEEDINGS{7314574,
author={A. T. Lopes and E. de Aguiar and T. Oliveira-Santos},
booktitle={2015 28th SIBGRAPI Conference on Graphics, Patterns and Images},
title={A Facial Expression Recognition System Using Convolutional Networks},
year={2015},
pages={273-280},
doi={10.1109/SIBGRAPI.2015.14},
ISSN={1530-1834},
month={Aug}
}