Accurate positioning method for human eye on the basis of gray gradation information

Abstract

The invention relates to an accurate positioning method for human eye on the basis of gray gradation information, belonging to the technical field of information. The method comprises: (1) human face detection: the area of a human face is positioned from an input image; (2) gray gradation column diagram analysis: gray gradation column diagram analysis is carried out on the human face image to determine the gray gradation range of the complexion area of the human face; (3) image strengthening: gray gradation adjustment is carried out on the image to enable eye characteristics to be more obvious; (4) Gabor wavelet filtration: Gabor wavelet filtration is carried out on the strengthened image, a real part image filtered by Gabor is synthesized to obtain a reference image; (5) cluster analysis: the synthesized reference image is analyzed by using the K-Means cluster method to obtain a binarization human eye candidate window, and the white area in each human eye candidate window after binarization is analyzed to determine the rough position of the human eye, so that the human eye window is obtained; (6) neighbourhood operation: the human eye window is scanned by two neighbourhood operators to determine the canter position of the pupilla. The invention has the advantages of simple detection method and accurate eye positioning.

Description

Technical field: [0001] The invention relates to a method for precise positioning of human eyes based on grayscale information, which belongs to the field of information technology. Background technique: [0002] The construction of automatic face recognition system has become a hot research field in pattern recognition and computer vision. Generally speaking, the automatic face recognition system mainly includes two parts, namely face detection and face recognition. At present, both parts have some effective methods, although face detection has achieved relatively good results. However, due to the various sizes and different rotation angles of the face, we must normalize the detected face image before performing face recognition. Among the various facial features of the face, the eyes It is the preferred feature for face normalization. In face recognition, the accuracy of human eye positioning has an important impact on the effect of face recognition. The existing human ey...

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Problems solved by technology

Generally speaking, the automatic face recognition system mainly includes two parts, namely face detection and face recognition. At present, both parts have some effective methods, although face detection has achieved relatively good results. However, due to the various sizes and different rotation angles of the face, we must normalize the detected face image before performing face recognition. Among the various facial features of the face, the eyes It is the preferred feature for face normalization. In face recognition, the accuracy of human eye positioning has an important impact on the effect of face recognition. The existing human eye detection methods cannot accurately locate the eyes. Especially when the resolution of the face image is low and the noise interference is large, the positioning error is often large, which affects the performance of the subsequent face recognition system.

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