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Human face recognition method based on anisotropic double-tree complex wavelet package transforms

A wavelet packet transform, anisotropic technology, applied in the field of pattern recognition, can solve the problems of low reliability, poor stability of face recognition, and large difference in image data quality, and achieves small calculation amount, reduced calculation amount, and good stability. sexual effect

Inactive Publication Date: 2008-09-24
TSINGHUA UNIV
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Problems solved by technology

[0008] 5) Of course, face recognition also has disadvantages such as poor stability, low reliability, and large differences in the quality of the collected image data. This is mainly because the face will change with age, makeup, lighting, viewing angle, distance and other conditions. big changes
At present, no one has used the dual-tree complex wavelet transform for face recognition

Method used

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  • Human face recognition method based on anisotropic double-tree complex wavelet package transforms
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  • Human face recognition method based on anisotropic double-tree complex wavelet package transforms

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Embodiment Construction

[0049] The face recognition method based on anisotropic dual-tree complex wavelet packet transform proposed by the present invention includes three parts: processing of average face, face feature extraction and classification judgment. The flow chart is as follows: figure 2 Shown, in conjunction with accompanying drawing and embodiment describe in detail as follows:

[0050] 1) The processing of the average face specifically includes the following steps:

[0051] 10) averaging the regular frontal gray-scale face images of multiple (generally more than dozens) different people in the regular face database to obtain the average face;

[0052] 11) The average face image is carried out with a wavelet lifting scheme to carry out J-level (J is a positive integer) dual-tree complex wavelet transform to obtain wavelet subbands at all levels, and the real and imaginary parts of the wavelet subbands have the J+1th level respectively. 2 low-frequency wavelet sub-bands and 6 high-freque...

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Abstract

The invention relates to a human face recognition method based on the anisotropy dual-tree complex wavelet packet transformation and belongs to a mode recognition technology field. The method includes that: firstly, an average face is processed; the characteristics of the face represented by a wavelet amplitude coefficient is obtained through a characteristic extraction of an input face image; a weight coefficient is used for the weighting of every wavelet sub-band amplitude coefficient, the same treatment is implemented on every regular front gray face image in a regular face database, and a standard face characteristic database is obtained; the face characteristics with wavelet amplitude coefficient corresponding to the input face image to be recognized matches one-to-one with the face characteristics with wavelet amplitude coefficient corresponding to every face image in the regular face characteristic database, and the face with the maximum similarity in the regular face characteristic database is used as the result of the face recognition. The face recognition method based on the anisotropy dual-tree complex wavelet packet transformation has the advantages of high face recognition accuracy as well as low computational complexity.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a face recognition method based on anisotropic dual-tree complex wavelet packet transformation. Background technique [0002] Identification and verification are very important in our daily life in today's human society. The identification and verification of human identity by using the physiological characteristics or behavioral characteristics that human beings possess that can uniquely mark their identity is called biometric identification. These biological characteristics include physical characteristics and behavioral characteristics. Physiological characteristics include face, fingerprint, iris, retina, palmprint, hand shape, DNA, ear shape, etc.; behavioral characteristics include human handwriting, voiceprint, gait, etc. These physiological and behavioral characteristics meet the uniqueness and long-term stability of individuals at least to a cer...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
Inventor 谢旭东彭义刚徐文立
Owner TSINGHUA UNIV
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