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Man face characteristic point positioning method of combining local searching and movable appearance model

An appearance model and facial feature technology, applied in character and pattern recognition, computer parts, instruments, etc., can solve problems such as errors, inability to give correct positioning, and fewer iterations, and achieve high speed, improved speed, and high performance. The effect of accuracy

Inactive Publication Date: 2005-10-26
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology allows for accurate and quicker identification of facial areas that help improve image processing techniques such as eye tracking or lip-based methods like those described earlier. It also includes an active look up table (ALT) database containing many unique locations on each person's skin called bonemarks. These location data helps identify specific parts of their body better than other places they were previously identified. By combining these two technologies together, it provides more precise and efficient ways to locate faces accurately while still maintaining strong visual recognition capabilities over time.

Problems solved by technology

The technical problem addressed in this patented text relates to improving the precision and efficiency of detecting faces or parts like ears accurately within an electronic device's camera sensor array. Current methods have limitations due to factors including lack of sufficient data sources with specific characteristics needed for precise identification of certain areas.

Method used

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Embodiment Construction

[0012] The technical solution of the present invention will be further described in detail below in conjunction with specific embodiments.

[0013] The face images used in the embodiment come from the captured face image database. Whole invention realization process is as follows:

[0014] 1. Establish an AAM model. Select n images from the face library as training samples, manually mark k feature points on the selected face images, these feature points in each image form a vector, and pass the n vectors through affine transformation, Including rotation, translation, and scaling to make it closest to the first vector, so that n new vectors are obtained, the average of these n vectors is calculated, and the average is transformed to make it the closest to the first vector Close to get a new average, and then take the average as the reference shape, make n vectors through affine transformation to make it the closest to the average, repeat the process until convergence, so that...

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Abstract

The invention is a method for locating characteristic points of a human face, integrated with local search and active appearance model, firstly using a part of a picture of human face with the coordinates of characteristic points as a sample to build a new picture of human face for detecting, so as to obtain a rectangular region containing the human face, locating eyes and mouth in the rectangular region, using the positions of the eyes and mouth as initial positions, searching by the active appearance model, and finally finding many characters points of human face, and thus completing the integral location of the characteristic points of human face. The method can be further applied to recognizing human face, sex, and expression, estimating age, and other aspects.

Description

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Claims

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Application Information

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Owner SHANGHAI JIAO TONG UNIV
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