Two-channel convolutional neural network for facial expression recognition using facial parts

Wang, Hui; Lu, Jiang; Nwosu, Lucy; Unwala, Ishaq

doi:10.1504/ijbdi.2019.10021734

Cited by 2 publications

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“…e results show that they have good practicability and effectiveness. Unwala et al [18] proposed a facial expression recognition system based on depth CNN.…”

Section: Related Workmentioning

confidence: 99%

Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction

Shengbin

2021

Journal of Robotics

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Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to output the expression types with high accuracy. Finally, the expression recognition method combined with the improved VGG16 and CNN model is applied to the human-computer interaction of the NAO robot. The robot makes different interactive actions according to different expressions. The experimental results based on CK + dataset show that the improved VGG16 network has strong supervised learning ability. It can extract features well for different expression types, and its overall recognition accuracy is close to 90%. Through multiple tests, the interactive results show that the robot can stably recognize emotions and make corresponding action interactions.

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“…e results show that they have good practicability and effectiveness. Unwala et al [18] proposed a facial expression recognition system based on depth CNN.…”

Section: Related Workmentioning

confidence: 99%

Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction

Shengbin

2021

Journal of Robotics

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Multi‐angle face expression recognition based on generative adversarial networks

2021

Computational Intelligence

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Because of the different features of the same facial expressions from different angles, most of the methods are only suitable for face images, and the accuracy of facial expression recognition is low. Therefore, a multi-angle facial expression recognition method based on generative adversarial networks (GAN) is proposed.Firstly, the depth regression network is used to detect the key points of the face image to achieve face alignment, so as to reduce the difficulty of feature extraction. Then, the image is input to GAN. The generator is composed of encoder and decoder, and a skip connection is designed.In the encoding phase, the generator can unlock the correlation between the facial expression image and the angle, and in the decoding stage, it can generate different angle facial expression images by adding other angle information. Finally, the multi-angle facial expression images are sent to the convolution neural network for classification and learning, in which the loss weight is adjusted dynamically to improve the recognition accuracy by introducing resistance loss, recognition loss, content loss, and center loss. The experimental results on Multi-pose illumination expression (PIE) and celebrities in frontal profile (CFP) datasets show that the performance of the proposed method is the best when the learning rate is 0.0002, and the recognition accuracy 20

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Two-channel convolutional neural network for facial expression recognition using facial parts

Cited by 2 publications

References 0 publications

Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction

Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction

Multi‐angle face expression recognition based on generative adversarial networks

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