Landmark guidance independent spatio-channel attention and complementary context information based facial expression recognition

Gera, Darshan; Balasubramanian, Siva

doi:10.1016/j.patrec.2021.01.029

Cited by 77 publications

(57 citation statements)

References 6 publications

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“…Precision/% Literature [18] 81.35 Literature [19] 84.48 Literature [20] 87.64 Literature [23] 88.12 Literature [24] 88.24 Literature [25] 86.47 e proposed method…”

Section: Methodsmentioning

confidence: 99%

Application of Convolutional Neural Network in Emotion Recognition of Ideological and Political Teachers in Colleges and Universities

Gao

2022

Scientific Programming

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With the update of Internet technology and the development of we-media, ideological and political education in colleges and universities has been greatly impacted. Higher requirements are put forward for ideological and political teachers in colleges and universities, whose emotions seriously affect the quality and effect of teaching. Aiming at the problems of poor network generalization ability and large computation amount caused by many network parameters in the existing emotion recognition methods, a face emotion recognition method based on convolutional neural network is proposed. The network structure of nested Maxout multilayer perceptron layer is constructed by optimizing the convolutional neural model. Maxout can enhance the feature extraction capability of the convolutional layer of convolutional neural network. Meanwhile, Maxout performs linear combination of target features to select the most effective feature information. Then, the pretraining model is used for emotion recognition training. The strong perception ability of the model for facial features is retained by changing the important parameters. Simulation results demonstrate that this method has a higher recognition rate of face emotion and can effectively achieve accurate face emotion classification.

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“…Precision/% Literature [18] 81.35 Literature [19] 84.48 Literature [20] 87.64 Literature [23] 88.12 Literature [24] 88.24 Literature [25] 86.47 e proposed method…”

Section: Methodsmentioning

confidence: 99%

Application of Convolutional Neural Network in Emotion Recognition of Ideological and Political Teachers in Colleges and Universities

Gao

2022

Scientific Programming

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“…e lower the loss value was, the better the robustness of the model was. e cross-entropy loss function was used to compute the loss value, which was showed as formula (3), where y represented the true classification value, a represented the predicted value, and c represented the loss value:…”

Section: Evaluation Indexmentioning

confidence: 99%

“…In different environments, facial expressions have many functions for communication. We can adjust dialogue, convey biological information, express mental labor intensity, and express emotions by receiving signals in turn [2,3]. In the process of information exchange between people, facial expressions play an indispensable role.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Method Combined with Attention Mechanism

Chen

Cheng²,

Zhang

et al. 2021

Mobile Information Systems

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Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed. Firstly, group convolution is used to reduce network parameters. The channels of traditional convolution are grouped to cut off redundant connections so that the number of parameters decreases significantly. Secondly, the ERFNet network model was improved by combining the asymmetric residual module and the weak bottleneck module to improve the running speed and reduce the loss of accuracy. Finally, the attention mechanism was added into the feature extraction network to improve the recognition precision. The experiment shows that compared with traditional face recognition methods, the proposed method can improve the recognition precision and recall significantly; in CK+, Jaffe, and Fer2013 datasets, the recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.

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“…DNNs also handle well new challenges in the uncontrolled environment including occlusions and pose variations [9,10,11,12]. However, these networks are deep, imbedded with a large number of parameters (for e.g.…”

Section: Introductionmentioning

confidence: 99%

“…However, these networks are deep, imbedded with a large number of parameters (for e.g. 70M parameters in [9]). Such networks are unfit to be deployed in real scenarios.…”

Section: Introductionmentioning

confidence: 99%

Imponderous Net for Facial Expression Recognition in the Wild

Gera¹,

Balasubramanian²

2021

Preprint

Self Cite

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Since the renaissance of deep learning (DL), facial expression recognition (FER) has received a lot of interest, with continual improvement in the performance. Hand-in-hand with performance, new challenges have come up. Modern FER systems deal with face images captured under uncontrolled conditions (also called in-the-wild scenario) including occlusions and pose variations. They successfully handle such conditions using deep networks that come with various components like transfer learning, attention mechanism and local-global context extractor. However, these deep networks are highly complex with large number of parameters, making them unfit to be deployed in real scenarios. Is it possible to build a light-weight network that can still show significantly good performance on FER under in-the-wild scenario? In this work, we methodically build such a network and call it as Imponderous Net. We leverage on the aforementioned components of deep networks for FER, and analyse, carefully choose and fit them to arrive at Imponderous Net. Our Imponderous Net is a low calorie net with only 1.45M parameters, which is almost 50x less than that of a state-of-the-art (SOTA) architecture. Further, during inference, it can process at the real time rate of 40 frames per second (fps) in an intel-i7 cpu. Though it is low calorie, it is still power packed in its performance, overpowering other light-weight architectures and even few high capacity architectures. Specifically, Imponderous Net reports 87.09%, 88.17% and 62.06% accuracies on in-the-wild datasets RAFDB, FERPlus and AffectNet respectively. It also exhibits superior robustness under occlusions and pose variations in comparison to other light-weight architectures from the literature.

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Landmark guidance independent spatio-channel attention and complementary context information based facial expression recognition

Cited by 77 publications

References 6 publications

Application of Convolutional Neural Network in Emotion Recognition of Ideological and Political Teachers in Colleges and Universities

Application of Convolutional Neural Network in Emotion Recognition of Ideological and Political Teachers in Colleges and Universities

Facial Expression Recognition Method Combined with Attention Mechanism

Imponderous Net for Facial Expression Recognition in the Wild

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