FaceShop: Deep Sketch-based Face Image Editing

We present a novel system for sketch-based face image editing, enabling users to edit images intuitively by sketching a few strokes on a region of interest. Our interface features tools to express a desired image manipulation by providing both geometry and color constraints as user-drawn strokes. As an alternative to the direct user input, our proposed system naturally supports a copy-paste mode, which allows users to edit a given image region by using parts of another exemplar image without the need of hand-drawn sketching at all. The proposed interface runs in real-time and facilitates an interactive and iterative workflow to quickly express the intended edits. Our system is based on a novel sketch domain and a convolutional neural network trained end-to-end to automatically learn to render image regions corresponding to the input strokes. To achieve high quality and semantically consistent results we train our neural network on two simultaneous tasks, namely image completion and image translation. To the best of our knowledge, we are the first to combine these two tasks in a unified framework for interactive image editing. Our results show that the proposed sketch domain, network architecture, and training procedure generalize well to real user input and enable high quality synthesis results without additional post-processing.Comment: 13 pages, 20 figure

Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images

Iris centre localization in low-resolution visible images is a challenging problem in computer vision community due to noise, shadows, occlusions, pose variations, eye blinks, etc. This paper proposes an efficient method for determining iris centre in low-resolution images in the visible spectrum. Even low-cost consumer-grade webcams can be used for gaze tracking without any additional hardware. A two-stage algorithm is proposed for iris centre localization. The proposed method uses geometrical characteristics of the eye. In the first stage, a fast convolution based approach is used for obtaining the coarse location of iris centre (IC). The IC location is further refined in the second stage using boundary tracing and ellipse fitting. The algorithm has been evaluated in public databases like BioID, Gi4E and is found to outperform the state of the art methods.Comment: 12 pages, 10 figures, IET Computer Vision, 201

Robust Eye Gaze Estimation

Eye gaze detection under challenging lighting conditions is a non-trivial task. Pixel intensity and the shades around the eye region may change depending on the time of day, location, or due to artificial lighting. This paper introduces a lighting-adaptive solution for robust eye gaze detection. First, we propose a binarization and cropping technique to limit our region of interest. Then we develop a gradient-based method for eye-pupil detection; and finally, we introduce an adaptive eye-corner detection technique that altogether lead to robust eye gaze estimation. Experimental results show the outperformance of the proposed method compared with related techniques

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation

EyePACT: eye-based parallax correction on touch-enabled interactive displays

The parallax effect describes the displacement between the perceived and detected touch locations on a touch-enabled surface. Parallax is a key usability challenge for interactive displays, particularly for those that require thick layers of glass between the screen and the touch surface to protect them from vandalism. To address this challenge, we present EyePACT, a method that compensates for input error caused by parallax on public displays. Our method uses a display-mounted depth camera to detect the user's 3D eye position in front of the display and the detected touch location to predict the perceived touch location on the surface. We evaluate our method in two user studies in terms of parallax correction performance as well as multi-user support. Our evaluations demonstrate that EyePACT (1) significantly improves accuracy even with varying gap distances between the touch surface and the display, (2) adapts to different levels of parallax by resulting in significantly larger corrections with larger gap distances, and (3) maintains a significantly large distance between two users' fingers when interacting with the same object. These findings are promising for the development of future parallax-free interactive displays

К проблеме анализа видеоизображений в задачах бесконтактного выявления потенциально опасных лиц

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Visual focus of attention estimation using eye center localization

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