Face recognizing and aligning method based on shared convolutional characteristic

Abstract

The invention provides a face recognizing and aligning method based on a shared convolutional characteristic. The method comprises four steps of firstly performing multilayer convolution and pooling operation on an input image, and extracting a convolutional characteristic; performing face recognition according to the extracted convolutional characteristic, and outputting face position information in the image; then according to the face position information which is input in the previous step, extracting the convolutional characteristic which corresponds with the face position, performing face characteristic point positioning on the face, and outputting face characteristic point positions such as eyebrows, canthuses, nose and mouth corners in the face image; and performing rotating and scaling processing on the face according to a face image and corresponding face characteristic point points which are output in the face characteristic point detecting step, and outputting the aligned face image. The face recognizing and aligning method can realize automatic detection and automatic alignment for the face in the image and has advantages of high speed and high accuracy. Furthermore the method facilitates accuracy improvement of face recognition and face identification technology.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a face detection and alignment method based on shared convolution features. Background technique [0002] With the continuous development of computer science, human-computer interaction has become an increasingly important technology. As the face recognition and face verification technology in the field of computer vision, it has begun to be applied in the industry. In the past few decades, face recognition and face verification technology has been a hot research topic in the field of computer vision. Face detection and alignment is a crucial step in face recognition. [0003] First of all, the relatively common face detection method currently used is the face detection method based on Haar-like features and AdaBoost technology, which trains the face detector based on AdaBoost technology by extracting artificially designed features. However, because Haar-like features a...

AI Technical Summary

Problems solved by technology

This method is not robust to abnormal feature points and pose changes, so it is difficult to get accurate face feature points, which will directly affect the face alignment effect and further lead to a serious decline in face recognition performance

[0005] Thirdly, although the method based on the convolutional neural network is higher than the traditional method in terms of accuracy, the convolutional neural network has a large amount of calculation and takes a long time to process a single image. It is difficult to meet the requirements of real-time face detection and alignment.

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