In this paper, we propose an automatic photograph generating method for albums of large numbers of people, which gives consistency to facial skin color and background colors. In our framework, we assume photography in a simple photographic environment where some amateur cameramen take photographs indoors. In such a case, facial skin color is often distorted due to the lighting environment (e.g., light reflected from a colored rear wall), and if the photo is artificially combined with another background, the difference in color is emphasized, resulting in an unnatural synthesized result.In our technique, after roughly extracting the facial region and rectifying the distribution of the skin color in a color space, we perform correction of the color and brightness near the face of the original image so that it matches the color balance of the facial image after the color correction. Unlike conventional algorithms for color correction, the final result is attained by a correction process with a guide image. We show through experimental results that more natural results are obtained than conventional methods.
In the guided filter that performs operations such as denoising and contrast correction with the help of a guide image, the positions of corresponding subjects need to be completely aligned, otherwise the misaligned regions in the output image are deteriorated by blur. In this paper, we propose a guided filter for images which include moving dynamic regions. Our filter uses correspondences of local covariance matrices instead of using the conventional pixel-topixel correspondences. In addition, we also propose a classification method to detect the dynamic regions by using the support vector machine. Combining two kinds of guided filters for static / dynamic regions, more natural resulting images are obtained.
This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe). This color distortion is emphasized when artificially synthesized with another background color, and the appearance becomes unnatural. In our framework, we, first, roughly extract the face region and rectify the skin color distribution in a color space. Then, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Our color correction process attains natural results by using a guide image, unlike conventional algorithms. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as another application.
This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe), and if the photo is artificially combined with another background color, this color change is emphasized, resulting in an unnatural synthesized result. In our framework, after roughly extracting the face region and rectifying the skin color distribution in a color space, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Unlike conventional algorithms for color correction, our final result is attained by a color correction process with a guide image. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as an another application.
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