Human face feature extracting method based on active shape model and POEM (patterns of oriented edge magnituedes) texture model in complicated background

Abstract

The invention relates to a human face feature extracting method based on an active shape model and a POEM (patterns of oriented edge magnituedes) texture model in the complicated background and belongs to the technical field of model identification. The human face feature extracting method includes calibrating feature points of a training set; establishing an overall shape model for training samples; establishing a POEM texture histogram for each calibrated feature point; selecting initial human face shapes of a factor selection model according to the shape model; calculating the POEM histogram of each candidate feature point in a test image; calculating similarity of the candidate feature points and target points of the histogram by the mahalanobis distance function; performing iterative search matching by downloading initial human faces into the model; secondarily extracting local organs or human face outlines with poor extraction effect. By the human face feature extracting method, robustness of changes of complicated environments (such as posture, light and expression) is improved, high extraction accuracy is obtained, and the human face feature extracting method has good application prospect.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and relates to a face feature positioning method based on an active shape model and a POEM texture model under a complex background. Background technique [0002] In recent years, face recognition has attracted the attention of a large number of scholars because of its simple acquisition equipment, convenient and fast process, and intuitive effects. It involves the knowledge of many disciplines such as pattern recognition, image processing, computer vision, physiology, psychology and cognition, and is closely related to the research fields of identification methods based on other biological characteristics and computer human-computer perception interaction. The face recognition process is generally divided into three steps: face detection, feature location and extraction, and classification recognition. Among them, face feature location is the basis of the whole face recognition meth...

AI Technical Summary

Problems solved by technology

However, when the face has posture, expression or illumination changes, the positioning accuracy of ASM will be affected, mainly in three aspects: 1) It is greatly affected by the initial shape of the search, when the initial shape is far from the target shape , the wrong target shape will be obtained; 2) use the gray information to represent the local texture features, and only use the gray information on the normal line of the feature point to represent the texture information of the point, ignoring the information of other methods. , noise and other noise resistance is weak; 3) easily affected by noise and local deformation, when the face has posture and expression changes, it is easy to fall into the local minimum situation

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