Automatic Face Recognition of Target Images Based on Deep Learning Algorithms

Abstract

The current research results of face recognition have poor recognition accuracy, time-consuming and high information loss rate. A method of automatic face recognition based on depth learning algorithm is proposed. The three primary RGB images are transformed into color saturated HSV images, and the dynamic range is extended by changing the non-linearity according to the conversion results. Based on the extended situation, the membership function is used to map the target image to the blurred plane, and the contrast enhancement of the image is completed. The enhanced image is substituted into the image segmentation, and the image eigenvalues are extracted preliminarily. The seed of image segmentation is generated by the eigenvalues. The growing seed regions are merged by the color distance and the texture distance to achieve the target image segmentation. Based on image segmentation, LBP operator is used to extract the local texture features of face twice, and then a deep convolution network model is constructed. The shared weights of convolution network model and pooling down-sampling technique are used to reduce the complexity of the model. The top layer of the model forms a feature classification surface of face image, fuses the constraint conditions, and obtains the trained face. The deep convolution network model is used to extract features from face images and complete face recognition. The experimental results show that the method has good accuracy, high efficiency and low information loss rate.