Facial Expression Recognition with Faster R-CNN

Li, Jiaxing; Zhang, Dexiang; Zhang, Jingjing; Zhang, Jun; Li, Teng; Xia, Yi; Yan, Qing; Xun, Lina

doi:10.1016/j.procs.2017.03.069

Cited by 87 publications

(34 citation statements)

References 4 publications

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“…The start with such a soft pooling is least destructive in relation to image specificities. Then further increment of MPLs is almost always possible, making training faster and accuracy higher (because the trained CNN receives its generalisation property [2], [3], [7], [15], [20], [21]). The represented rule is believed to be reliable owing to diversity and heterogeneousness of the used datasets.…”

Section: Discussionmentioning

confidence: 99%

Appropriate Number of Standard 2 × 2 Max Pooling Layers and Their Allocation in Convolutional Neural Networks for Diverse and Heterogeneous Datasets

Romanuke

2017

Information Technology and Management Science

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-A problem of appropriately allocating pooling layers in convolutional neural networks is considered. The consideration is based on CIFAR-10, NORB, and EEACL26 datasets for preventing "overfitting" in a solution of the problem. For highly accurate image recognition within these datasets, the networks are used with the max pooling operation. The most common form of such operation, which is a 2 2  pooling layer, is applied with a stride of 2 without padding after convolutional layers. Based on performance against a series of the network architectures, a rule for the best allocation of max pooling layers is formulated. The rule is to insert a few pooling layers after the starting convolutional layers and to insert a one pooling layer after the last but one convolutional layer ("11...100...010"). For much simpler datasets, the best allocation is "11...100...0".

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Section: Discussionmentioning

confidence: 99%

Appropriate Number of Standard 2 × 2 Max Pooling Layers and Their Allocation in Convolutional Neural Networks for Diverse and Heterogeneous Datasets

Romanuke

2017

Information Technology and Management Science

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“…They also proposed two models that are suitable for hardware deployment that sustainable to the change in the kernel size. Li and others in [7], designed a faster region-based convolutional neural network in which the dimensions of the extracted features are reduced using maximum pooling. This network includes the region proportional networks which help to detect the region with which the expression can be recognized accurately.…”

Section: Related Workmentioning

confidence: 99%

Illumination Invariant Facial Expression Recognition using Convolutional Neural Networks

Rao*¹,

Rao²

2019

IJRTE

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In this work, we propose a prospective novel method to address illumination invariant system for facial expression recognition. Facial expressions are used to convey nonverbal visual information among humans. This also plays a vital role in human-machine interface modules that have invoked attention of many researchers. Earlier machine learning algorithms require complex feature extraction algorithms and are relying on the size and uniqueness of features related to the subjects. In this paper, a deep convolutional neural network is proposed for facial expression recognition and it is trained on two publicly available datasets such as JAFFE and Yale databases under different illumination conditions. Furthermore, transfer learning is used with pre-trained networks such as AlexNet and ResNet-101 trained on ImageNet database. Experimental results show that the designed network could recognize up to 30% variation in the illumination and it achieves an accuracy of 92%.

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“…For example, Sun et al [25] applied the region‐based CNN (R‐CNN) to extract features for FER. By generating high‐quality region proposals, Li et al [26] used faster R‐CNN to identify facial expressions. Moreover, with the sequence image, Li et al [27] proposed 3D CNN to capture the motion information that encoded in multiple adjacent frames.…”

Section: Introductionmentioning

confidence: 99%

Novel multi‐scale deep residual attention network for facial expression recognition

Dong

Wang

et al. 2020

J. eng.

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Facial Expression Recognition with Faster R-CNN

Cited by 87 publications

References 4 publications

Appropriate Number of Standard 2 × 2 Max Pooling Layers and Their Allocation in Convolutional Neural Networks for Diverse and Heterogeneous Datasets

Appropriate Number of Standard 2 × 2 Max Pooling Layers and Their Allocation in Convolutional Neural Networks for Diverse and Heterogeneous Datasets

Illumination Invariant Facial Expression Recognition using Convolutional Neural Networks

Novel multi‐scale deep residual attention network for facial expression recognition

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