Abstract
Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: First, we use a generator composed of an autoencoder and the adversarial learning of two discriminators (local discriminator and global discriminator) to fill and repair an occluded face image. On this basis, the Resnet-50 network is used to perform image restoration on the face. In our recognition framework, we introduce a classification loss function that can quantify the distance between classes. The image generated by the generator can only capture the rough shape of the missing facial components or generate the wrong pixels. To obtain a clearer and more realistic image, this paper uses two discriminators (local discriminator and global discriminator, as mentioned above). The images generated by the proposed method are coherent and minimally influence facial expression recognition. Through experiments, facial images with different occlusion conditions are compared before and after the facial expressions are filled, and the recognition rates of different algorithms are compared. Results: The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. When the occlusion area is less than 50%, the overall recognition rate of the model is above 80%, which is close to the recognition rate pertaining to the non-occluded images. Conclusions: The experimental results show that the method in this paper has a better restoration effect and higher recognition rate for face images of different occlusion types and regions. Furthermore, it can be used for face recognition in a daily occlusion environment, and achieve a better recognition effect.
Highlights
With the development of artificial intelligence technology, biometric recognition technology has received unprecedented attention
The occlusion area is random occlusion, where the first line is the original image before occlusion, the second line is the face image after occlusion and the third line is the result of face filling and image restoration
When the occlusion area is less than 50%, CNN method, DCGAN is used to fill the occluded face image, and the CNN is a finethe overall recognition rate of the model is still above 80%, which is close to 20% higher tuned VGG model
Summary
With the development of artificial intelligence technology, biometric recognition technology has received unprecedented attention. With the gradual improvement of machine learning technology, many powerful algorithms have been developed, such as the genetic algorithm, Bayesian classifier and support vector machines The application of these algorithms [3] in face recognition technology improves the accuracy of face recognition to a certain extent, but its feature extraction is complex and single, especially when the faces are occluded, which is greatly affected by human factors. Traditional methods are usually used for partial occlusion face recognition, and the ideas are generally divided into two types: the discarding method and the filling method
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
