抄録
In this paper, we propose an attribute added face image generation system using Deep Convolutional Generative Adversarial Networks (DCGANs). Convolutional Neural Networks (CNNs) can extract important features of an image and attain high precision in image classification tasks. In the proposed system, image features are extracted using CNNs, and attribute features added to image features, and attributes added images are generated by DCGANs. Specifically, we use the attributes of “smile” and “male”, and work on a task of generating smile images from non-smile images, and a task of generating male images from women images. Since the training of the proposed system requires image pairs including with and without attributes, we use two extraction methods using attribute label and cosine similarity. Attribute features are defined as the averaged difference between image features with and without attributes. We performed two kinds of evaluation experiments, and excellent characteristics were obtained.
