Objective

2

China has a large population of 1.3 billion in 2005

accounting for 19% of the world's population. This number is equivalent to the population of Europe or Africa added to the populations of Australia

North America

and Central America. It is one of the few populous countries in the world

and its huge population size has brought many problems. With the rapid development of the economy

the number of people working outside of their homes is increasing

the population is moving frequently

and the safety of the floating population is even difficult to control. The huge mobile population provides the city's infrastructure and public services tremendous pressure. Thus

to conduct a comprehensive check on the area of adult traffic

which is time-consuming and labor-intensive

is difficult for security and related staff. Particularly

complex environmental safety problems

such as subways

railway stations

and airports

are becoming increasingly serious. Unstable events occur frequently

security situation is receiving much attention

and urban management and service systems are seriously lagging behind. These conditions need to be improved

especially after the September 11 incident in the United States. The situation has aroused widespread concern in the international community. Meanwhile

expression is the most intuitive way for humans to express emotions. In addition to language communication

expressions are extremely effective means of communication. People usually express their inner feelings through specific expressions. Expression can be used to judge other person's thoughts. For expressions used to express information

psychologist Mehrabian summed up a formula:emotional expression=7% of words + 38% of sound + 55% of facial expressions. Expression is one of the most important features of human emotion recognition. Expression is the emotional state expressed by facial muscle changes. Through the facial expression of the person's face

to evaluate abnormal psychological state

speculate on extreme emotions

and observe the facial expressions of pedestrians in the subway

railway station

and airport to further judge the psychology of the person is possible. We provide technical support to determine who is suspicious and prevent certain criminal activities in a timely manner. Strengthening urban surveillance and identifying the facial expressions of criminals are especially important. Expression plays an important role in human emotion cognition. However

factors affecting facial expression recognition in safety screening are extremely large

and the large intra-class gap seriously inflluences the accuracy of facial expression recognition. The problem of large gaps in facial expression recognition in a real environment is solved by identifying suspected molecules to be monitored should be identified

and the security personnel should prepare in advance to accurately identify them. Facial expressions are also particularly important for preventing security problems. The era of large data has arrived. Meanwhile

with the advancement of computer hardware

deep learning continues to develop. The traditional facial expression recognition method cannot meet the needs of the development of the times

and a new algorithm based on deep learning facial expression recognition is coming soon. Learning methods are widely used in facial expression recognition. Although facial recognition intelligent recognition technology has a long history of research

a large number of research methods have been proposed. However

due to the large facial expression gap

the expression is complex

and the influencing factors are many. The current intelligent recognition effect of facial expression results is not ideal. Considering the deep learning because of its powerful expressive ability

this study introduces the model structure of traditional neural network and carries out corresponding experiments and analysis in the context of real-life facial expression recognition and proposes real-world facial expression recognition research based on deep learning. In the next period

real-world facial expression recognition will make considerable progress. This work further studies the realistic facial expression recognition based on deep learning.

Method

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This study constructs a new IC-GAN(intra-class gap GAN(generative adversarial network)) recognition network model

providing good adaptability to the facial expression recognition task with large gap within the class. The network consists of a convolutional layer

a fully connected layer

an active layer

a BathNorm layer

and a Softmax layer

in which a convolutional assembly encoder and a decoder are used to perform deep feature extraction on facial expression images

and download and parse from the network. The video self-made mixed facial expression data set is based on the real environment

the image is expanded

and the facial expression data are normalized. The complexity of the facial expression features with large differences within the class also increased the network training and network recognition. The momentum-based Adam is used to update the network weight

adjust the network parameters

and optimize the network structure based on this factor. In this study

the facial expression category data are trained based on the Pytorch platform in deep learning and tested on the verification set of the self-made mixed facial expression data set.

Result

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When the input image is 256×256 pixels

the IC-GAN network model can reduce the false positive rate of the expression in large difference in the class

image blur

and facial expression incompleteness

and improve the system robustness. Compared with deep belief network(DBN) deep trust network and GoogLeNet network

the recognition result of IC-GAN network is 11% higher than that of DBN network and 8.3% higher than that of GoogLeNet network.

Conclusion

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The IC-GAN accuracy in facial expression recognition with large gaps in the class is verified by experiments. This condition reduces the misunderstanding rate of facial expressions in large intra-class differences

improves the system robustness

and lays down the solid foundation for facial expression generation.