RoboCoDraw: Robotic Avatar Drawing with GAN-Based Style Transfer and Time-Efficient Path Optimization

Robotic drawing has become increasingly popular as an entertainment and interactive tool. In this paper we present RoboCoDraw, a real-time collaborative robot-based drawing system that draws stylized human face sketches interactively in front of human users, by using the Generative Adversarial Network (GAN)-based style transfer and a Random-Key Genetic Algorithm (RKGA)-based path optimization. The proposed RoboCoDraw system takes a real human face image as input, converts it to a stylized avatar, then draws it with a robotic arm. A core component in this system is the AvatarGAN proposed by us, which generates a cartoon avatar face image from a real human face. AvatarGAN is trained with unpaired face and avatar images only and can generate avatar images of much better likeness with human face images in comparison with the vanilla CycleGAN. After the avatar image is generated, it is fed to a line extraction algorithm and converted to sketches. An RKGA-based path optimization algorithm is applied to find a time-efficient robotic drawing path to be executed by the robotic arm. We demonstrate the capability of RoboCoDraw on various face images using a lightweight, safe collaborative robot UR5.

Download Full-text

Evaluation of Generative Adversarial Network for Human Face Image Synthesis

2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) ◽

10.23919/softcom50211.2020.9238203 ◽

2020 ◽

Author(s):

Ivana Marin ◽

Sven Gotovac ◽

Mladen Russo

Keyword(s):

Image Synthesis ◽

Face Image ◽

Human Face ◽

Generative Adversarial Network ◽

Adversarial Network

Download Full-text

Matching Thermal to Visible Face Images Using a Semantic-Guided Generative Adversarial Network

2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019) ◽

10.1109/fg.2019.8756527 ◽

2019 ◽

Cited By ~ 3

Author(s):

Cunjian Chen ◽

Arun Ross

Keyword(s):

Generative Adversarial Network ◽

Adversarial Network ◽

Face Images

Download Full-text

Presentation Attack Face Image Generation Based on a Deep Generative Adversarial Network

Sensors ◽

10.3390/s20071810 ◽

2020 ◽

Vol 20 (7) ◽

pp. 1810

Author(s):

Dat Tien Nguyen ◽

Tuyen Danh Pham ◽

Ganbayar Batchuluun ◽

Kyoung Jun Noh ◽

Kang Ryoung Park

Keyword(s):

Recognition Task ◽

Recognition System ◽

Attack Detection ◽

Image Generation ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Face Images ◽

Problem Presentation ◽

Recognition Systems ◽

Public Datasets

Although face-based biometric recognition systems have been widely used in many applications, this type of recognition method is still vulnerable to presentation attacks, which use fake samples to deceive the recognition system. To overcome this problem, presentation attack detection (PAD) methods for face recognition systems (face-PAD), which aim to classify real and presentation attack face images before performing a recognition task, have been developed. However, the performance of PAD systems is limited and biased due to the lack of presentation attack images for training PAD systems. In this paper, we propose a method for artificially generating presentation attack face images by learning the characteristics of real and presentation attack images using a few captured images. As a result, our proposed method helps save time in collecting presentation attack samples for training PAD systems and possibly enhance the performance of PAD systems. Our study is the first attempt to generate PA face images for PAD system based on CycleGAN network, a deep-learning-based framework for image generation. In addition, we propose a new measurement method to evaluate the quality of generated PA images based on a face-PAD system. Through experiments with two public datasets (CASIA and Replay-mobile), we show that the generated face images can capture the characteristics of presentation attack images, making them usable as captured presentation attack samples for PAD system training.

Download Full-text

Hybrid Features Extraction for Adaptive Face Images Retrieval

International Journal of Synthetic Emotions ◽

10.4018/ijse.2020010102 ◽

2020 ◽

Vol 11 (1) ◽

pp. 17-26 ◽

Cited By ~ 1

Author(s):

Adel Alti

Keyword(s):

Feature Extraction ◽

Recognition Accuracy ◽

Face Image ◽

Human Face ◽

Gradient Vector ◽

Facial Image ◽

Hybrid Features ◽

Face Images ◽

Hybrid Feature Extraction ◽

Face Emotion Recognition

Existing methods of face emotion recognition have been limited in performance in terms of recognition accuracy and execution time. It is highly important to use efficient techniques for improving this performance. In this article, the authors present an automatic facial image retrieval combining the advantages of color normalization by texture estimators with the gradient vector. Starting from a query face image, an efficient algorithm for human face by hybrid feature extraction provides very interesting results.

Download Full-text

Face and Eye Detection

Automated Face Analysis ◽

10.4018/978-1-60566-216-9.ch002 ◽

2011 ◽

pp. 5-44 ◽

Cited By ~ 1

Author(s):

Daijin Kim ◽

Jaewon Sung

Keyword(s):

Face Detection ◽

Facial Expression Recognition ◽

Face Image ◽

Face Tracking ◽

Expression Recognition ◽

Human Face ◽

Eye Detection ◽

Face Image Analysis ◽

Face Images ◽

The Face

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

Download Full-text

Image Style Transfer based on Generative Adversarial Network

2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) ◽

10.1109/itnec48623.2020.9084750 ◽

2020 ◽

Author(s):

Chan Hu ◽

Youdong Ding ◽

Yuhang Li

Keyword(s):

Generative Adversarial Network ◽

Style Transfer ◽

Adversarial Network

Download Full-text

Synthesizing Depth Hand Images with GANs and Style Transfer for Hand Pose Estimation

Sensors ◽

10.3390/s19132919 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2919 ◽

Cited By ~ 2

Author(s):

Wangyong He ◽

Zhongzhao Xie ◽

Yongbo Li ◽

Xinmei Wang ◽

Wendi Cai

Keyword(s):

Pose Estimation ◽

Ground Truth ◽

Training Image ◽

Training Data ◽

Generative Adversarial Network ◽

Style Transfer ◽

Visual Appearance ◽

Hand Pose Estimation ◽

Adversarial Network ◽

Hand Pose

Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

Download Full-text

Complete Face Recovering: An Approach towards Recognizing a Person by a Single Partial Face Image without the Target Photo in Gallery

10.36227/techrxiv.12333176.v1 ◽

2020 ◽

Author(s):

Yiu-ming Cheung ◽

Mengke Li

Keyword(s):

Promising Result ◽

Face Image ◽

Attractive Potential ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Discriminative Feature ◽

New Variant ◽

Potential Applications ◽

Benchmark Datasets

Complete face recovering (CFR) is to recover the complete face image of a given partial face image of a target person whose photo may not be included in the gallery set. The CFR has several attractive potential applications but is challenging. As far as we know, the CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to addressing the CFR. First, a denoising auto-encoder based network is applied to acquire the discriminative feature. Then, we propose an identity-preserved loss function to keep the personal identity information. Furthermore, the acquired features are fed into a new variant of the generative adversarial network (GAN) to restore the complete face image. In addition, a two-pathway discriminator is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the promising result of the proposed approach.

Download Full-text