Special skills are required in portrait painting, such as imagining geometric structures and facial detail for final portrait designs. This makes it a difficult task for users, especially novices without prior artistic training, to draw freehand portraits with high-quality details. In this paper, we propose dualFace, a portrait drawing interface to assist users with different levels of drawing skills to complete recognizable and authentic face sketches. Inspired by traditional artist workflows for portrait drawing, dualFace gives two-stages of drawing assistance to provide global and local visual guidance. The former helps users draw contour lines for portraits (i.e., geometric structure), and the latter helps users draw details of facial parts, which conform to the user-drawn contour lines. In the global guidance stage, the user draws several contour lines, and dualFace then searches for several relevant images from an internal database and displays the suggested face contour lines on the background of the canvas. In the local guidance stage, we synthesize detailed portrait images with a deep generative model from user-drawn contour lines, and then use the synthesized results as detailed drawing guidance. We conducted a user study to verify the effectiveness of dualFace, which confirms that dualFace significantly helps users to produce a detailed portrait sketch.

Visual cryptography (VC) is an encryption technique for hiding a secret image in distributed and shared images (referred to as shares). VC schemes are employed to encrypt multiple images as meaningless, noisy patterns or meaningful images. However, decrypting multiple secret images using a unique share is difficult with traditional VC. We propose an approach to hide multiple images in meaningful shares. We can decrypt multiple images simultaneously using a common share, which we refer to as a magic sheet. The magic sheet decrypts multiple secret images depending on a given share. The shares are printed on transparencies, and decryption is performed by physically superimposing the transparencies. We evaluate the proposed method using binary, grayscale, and color images.