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A Smile Synthesis Method Based on Partial Sparse Component Analysis Model

A technology of sparse component analysis and synthesis method, applied in the field of smile synthesis, which can solve the problems of complex face modeling model, high computational complexity, and lack of robustness.

Active Publication Date: 2018-11-02
LIAOCHENG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In these methods, the face modeling model is too complex, the computational complexity is too high, and the learned face part is not flexible enough and lacks robustness

Method used

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  • A Smile Synthesis Method Based on Partial Sparse Component Analysis Model
  • A Smile Synthesis Method Based on Partial Sparse Component Analysis Model
  • A Smile Synthesis Method Based on Partial Sparse Component Analysis Model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] Embodiment 1: A method for synthesizing a smiley face based on a partial sparse component analysis model, which is implemented through the following steps:

[0061] Step 1. Learn to build a generative model for face representation.

[0062] The generative model finds a common spatial segmentation for a given face image and learns a sparse component analysis model for each part of this segmentation. Suppose the probability distribution of the production model is P(x, Z, R), and its calculation method is P(x, Z, R) = P(x|R, Z)P(R)P(Z). First, derive the continuous induction prior of the production model, and use PoE (product of experts) to combine the continuous induction prior and the polynomial prior to form P(R), namely:

[0063]

[0064] Among them, Z 0 is a normalization function, and the variable used to record the selection of face image pixels is r d. =(r d1 ,,r dK ) T , and r d. ~Mult(n=1,α d. ), D d is the variable r dk Adjacent variable set, P 1 (...

Embodiment 2

[0090] Embodiment 2; A kind of smiling face synthesis method based on the partial sparse component analysis model, concrete implementation steps are described in detail as follows (using Visual C++ language programming to realize):

[0091] 1. Learn the face part prior, and realize the partial representation of the face.

[0092] In this embodiment, the CBCL face database is used to learn the face part prior, and it is transferred to the smile synthesis experiment on the face and facial expression database. The number of face parts is set to K=6, and the number of bases is set to M=40. M works well over a large range (empirically M ∈ [20, 240]) and is able to learn reasonable priors on face parts. The basis learned by the face part is shown in Figure 2. For comparison, Figure 2 also lists the bases learned by the other two methods. These two methods are Multiple Cause Factor Analysis (MCFA) ) and Structured Sparse Principle Component Analysis (SSPCA). Among Fig. 2, Fig. 2 (...

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Abstract

The invention relates to a smiling face synthesis method based on a segment-type sparse component analysis model. The method comprises steps: firstly, the segment-type sparse component analysis model for face representation is derived; then, a rule for reconstruction and projection is given based on the model; a projection coefficient is obtained by using the projection rule, and the reconstruction rule is used for reconstructing an inputted face; the above projection and reconstruction process is repeated on the face after reconstruction; and finally, multiple face images after reconstruction are outputted as a smile synthesis process for the inputted face. The method has the significant effects that the synthesized face is basically reasonable and smooth, and the synthesized face has a sense of reality.

Description

technical field [0001] The invention relates to a smiling face synthesis method based on a partial sparse component analysis model, which can be widely used in the fields of film production, virtual community, game entertainment, animation synthesis and the like. It belongs to the field of computer vision, pattern recognition and human-computer interaction. Background technique [0002] As a medium for conveying human emotion and mental state, human face plays a very important function of information transmission and expression in social communication. The human face transmits various information to the outside world through rich facial expressions. In recent years, the use of computer technology to reconstruct and synthesize realistic facial expressions has become a research hotspot for researchers in the fields of computer vision, human-computer interaction and computer graphics. , and are widely used in advertising, animation, film and television and games industries. S...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T13/40
CPCG06T13/40G06T2207/20081G06T2207/30201
Inventor 王存刚王斌
Owner LIAOCHENG UNIV