人脸图像合成是新一代人机交互中的重要技术。
Facial expression synthesis is an important technique in human computer interactions.
长期以来，在计算机图形学、图像处理和计算机视觉这三个学科领域中，人脸的计算机模拟合成一直是个研究的热点。
Since a long time, facial image synthesis has been a hot research topic in the fields of computer graphics, image process and computer vision.
除了图像分析合成，模型基编码中还有很重要的一个部分就是特定人脸模型的生成。
Besides image analysis and synthesis, model-based coding research still has an important direction to reconstruct an individual facial model.
利用HMM的统计特性，对HMM模型结构进行改动，使其成为人脸语音动画合成中语音特征到图像特征的映射模型。
This paper takes advantage of HMM's statistic characters. With a little modification on HMM structure, it gets a mapping model from speech to image.
我们的方法能够较好地满足商图像方法的理论前提，从而达到更好的图像合成效果和人脸识别性能。
Our method can meet the theoretical prerequisites of quotient image method, and achieve better image synthesis and face recognition results.
实验结果表明：该方法能有效地合成无眼镜的正面人脸图像。 原始戴眼镜人脸图像的识别率是50.1%，合成的无眼镜正面人脸图像的识别率是99.4%。
Test results show that the method can effectively synthesize eyeglassless facial images, with the subseguent recognition rate increased from 50.1% to 99.4%.
该算法将人脸特征提取与图像复合相结合，无需3维人脸模型重建，自动合成具有源图像主要五官特征的结果图像。
This paper combines facial features extraction with image fusion algorithm, so that we can automatically obtain synthesis results without using 3d facial models.
最后通过对图像划分区域，分段完成纹理贴图，合成3d人脸模型。
Finally texture mapping is completed according to different regions on the face and thus the 3d face model is synthesized.
当给出一张测试人脸图像时，我们利用因素分解模型的“转移”算法合成测试人脸在训练集已有表情下的图像和训练集人脸在测试人脸表情下的图像。
By given a test image, the expressions in the training set can be "translate" to the input image by using factorization model and vice versa.
当给出一张测试人脸图像时，我们利用因素分解模型的“转移”算法合成测试人脸在训练集已有表情下的图像和训练集人脸在测试人脸表情下的图像。
By given a test image, the expressions in the training set can be "translate" to the input image by using factorization model and vice versa.
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