Feature space-based human face image representation and recognition

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

HUMAN FACE IMAGE PROCESSING TECHNIQUES

International Journal of Image and Graphics ◽

10.1142/s021946780100013x ◽

2001 ◽

Vol 01 (02) ◽

pp. 197-215 ◽

Cited By ~ 5

Author(s):

HONG YAN

Keyword(s):

Image Processing ◽

Research Work ◽

Facial Feature ◽

Face Image ◽

Computer Algorithms ◽

Human Face ◽

Feature Location ◽

Image Processing Techniques ◽

Face Image Processing ◽

Processing Techniques

Human face image processing techniques have many applications, such as in security operations, entertainment, medical imaging and telecommunications. In this paper, we provide an overview of existing computer algorithms for face detection and facial feature location, face recognition, image compression and animation. We also discuss limitations of current methods and research work needed in the future.

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

A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR

Applied Sciences ◽

10.3390/app8112242 ◽

2018 ◽

Vol 8 (11) ◽

pp. 2242 ◽

Cited By ~ 16

Author(s):

Bushra Zafar ◽

Rehan Ashraf ◽

Nouman Ali ◽

Muhammad Iqbal ◽

Muhammad Sajid ◽

...

Keyword(s):

Image Classification ◽

Spatial Information ◽

Image Representation ◽

Feature Space ◽

Research Problem ◽

Visual Words ◽

Spatial Image ◽

User Query ◽

Novel Approach ◽

Image Representations

The requirement for effective image search, which motivates the use of Content-Based Image Retrieval (CBIR) and the search of similar multimedia contents on the basis of user query, remains an open research problem for computer vision applications. The application domains for Bag of Visual Words (BoVW) based image representations are object recognition, image classification and content-based image analysis. Interest point detectors are quantized in the feature space and the final histogram or image signature do not retain any detail about co-occurrences of features in the 2D image space. This spatial information is crucial, as it adversely affects the performance of an image classification-based model. The most notable contribution in this context is Spatial Pyramid Matching (SPM), which captures the absolute spatial distribution of visual words. However, SPM is sensitive to image transformations such as rotation, flipping and translation. When images are not well-aligned, SPM may lose its discriminative power. This paper introduces a novel approach to encoding the relative spatial information for histogram-based representation of the BoVW model. This is established by computing the global geometric relationship between pairs of identical visual words with respect to the centroid of an image. The proposed research is evaluated by using five different datasets. Comprehensive experiments demonstrate the robustness of the proposed image representation as compared to the state-of-the-art methods in terms of precision and recall values.

Download Full-text