• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.176-188
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• 2022

In this paper, we propose a compact method for anthropomorphism that uses Deep Convolutional Neural Networks (DCNN) to detect the similarities between a human face and an animal face. We also apply texture feature-based morphing between them. We propose a basic texture feature-based morphing system for morphing between human faces only. The entire anthropomorphism process starts with the creation of an animal face classifier using a parallel DCNN that determines the most similar animal face to a given human face. The significance of our network is that it contains four sets of convolutional functions that run in parallel, allowing it to extract more features than a linear DCNN network. Our employed texture feature algorithm-based automatic morphing system recognizes the facial features of the human face and takes the Control Points automatically, rather than the traditional human aiding manual morphing system, once the similarity was established. The simulation results show that our suggested DCNN surpasses its competitors with a 92.0% accuracy rate. It also ensures that the most similar animal classes are found, and the texture-based morphing technology automatically completes the morphing process, ensuring a smooth transition from one image to another.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.213-220
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• 2018

Face morphing, one of the most powerful image processing techniques that are often used in image processing and computer graphic fields, as it is a technique to change the image progressively and naturally from the original image to the target image. In this paper, we propose a method to generate Delaunay triangles using the facial landmark vertices generated by the Dlib face landmark detector and to implement morphing through warping and cross dissolving of Delaunay triangles between the original image and the target image. In this paper, we generate vertex points for face not manually but automatically, which is the major feature of the face such as eye, eyebrow, nose, and mouth, and is used to generate Delaunay triangles automatically which is the main characteristic of our face morphing method. Simulations show that we can add vertices manually and get more natural morphing results.

• Journal of Korea Multimedia Society
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• v.22 no.5
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• pp.558-572
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• 2019

Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

• The KIPS Transactions:PartA
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• v.17A no.6
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• pp.281-288
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• 2010

In this paper, we describe the development of a system for generating a 3-dimensional human face using 3D scanned facial data and photo images, and morphing animation. The system comprises a facial feature input tool, a 3-dimensional texture mapping interface, and a 3-dimensional facial morphing interface. The facial feature input tool supports texture mapping and morphing animation - facial morphing areas between two facial models are defined by inputting facial feature points interactively. The texture mapping is done first by means of three photo images - a front and two side images - of a face model. The morphing interface allows for the generation of a morphing animation between corresponding areas of two facial models after texture mapping. This system allows users to interactively generate morphing animations between two facial models, without programming, using 3D scanned facial data and photo images.

• Journal of Korea Game Society
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• v.10 no.6
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• pp.137-146
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• 2010

According to rapid advent of the smartphone, software technologies which have been used on PCs are being introduced into the smartphone. This paper introduces an implementation of an application software under the Android platform using morphing and warping technology. It composes two face images with morphing technology and also transforms an original face image using warping technology for fun. For this, the image is triangulated by Delaunay Triangulation based on control points which are created by simple LCD touches on the mobile device. The implementation case of this paper will be a good reference for the development of applications like games using morphing and warping technologies over Android platforms.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.39-44
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• 2010

In this paper, we addressed a face transform scheme with age-progressing based on vector representation. Proposed approach utilized a vector modeling as well as morphing so as to improve not only a reliability but also a consistency. For the more, some elements of texture change owing to the face shape are defined and some parameters with respect to the internal and external environments are also considered. To testify the proposed approach, estimation of similarity is performed with qualitative manner by using experimental output, and finally resulted in satisfactory for face shape transformation aged from sixty to fourteen.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.435-443
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• 2018

Recently, with the explosive development of computing power, various methods such as RNN and CNN have been proposed under the name of Deep Learning, which solve many problems of Computer Vision have. The Generative Adversarial Network, released in 2014, showed that the problem of computer vision can be sufficiently solved in unsupervised learning, and the generation domain can also be studied using learned generators. GAN is being developed in various forms in combination with various models. Machine learning has difficulty in collecting data. If it is too large, it is difficult to refine the effective data set by removing the noise. If it is too small, the small difference becomes too big noise, and learning is not easy. In this paper, we apply a deep CNN model for extracting facial region in image frame to GAN model as a preprocessing filter, and propose a method to produce composite images of various facial expressions by stably learning with limited collection data of two persons.

• Smart Media Journal
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• v.4 no.1
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• pp.9-15
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• 2015

Nonverbal communications such as eye gazing, posture, and gestures send forceful messages. In regard to nonverbal communication, eye gazing is one of the most strong forms that an individual can use. However, lack of mutual gazing occurs when we use video conferencing system. The displacement between locations of the eyes and a camera gets in the way of eye contact. The lack of eye gazing can give unapproachable and unpleasant feeling. In this paper, we propose an eye gazing correction for video conferencing. We use two cameras installed at the top and the bottom of the television. The captured two images are rendered with 2D warping at virtual position. We implement view morphing to the detected face, and synthesize the face and the warped image. The result shows that eye gazing is corrected and correctly preserved and the image was synthesized seamlessly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.83-86
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• 2013

In this paper, by using the Morphing, if the size of the eyes of both sides are not the same, we propose a method to correct the size of eyes area. First, by using the Haar-like feature from a input image that is input, to detect the shape of the eyes and face. After inverting the left and right eye region of one of the shape of the eyes detected sets the correspondence between the second with a line to control the shape of the eyes detected using eyes that is detected with canny edge, in the previous step. To the Warping to match the correspondence was then set in the previous step, an area of each eye. Then, I merge the image which merged in the eye area is detected from the original image. As a result, a system result of the experiment in the test image and face image seen from the front, the proposed, prove to be more efficient than a method of keying the size of the eye only.

• Anxiety and mood
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• v.10 no.1
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• pp.37-43
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• 2014

Objective : Social Phobia is associated with extensive disability and reduced quality of life. The concept of 'social self' is a representation of the self-reflected in the eyes of others, and is recruited during self-face recognition, which is closely related to self-esteem. The aim of this study was to identify the relationship of face recognition for self and others using measures of social anxiety and self-esteem in patients with social phobia. Methods : Twenty-seven patients with social phobia and twenty-three normal controls were evaluated with scales of self-esteem, depression, anxiety and other psychiatric symptoms. All participants completed the self-face recognition task. Nine self-faces, nine other faces and eighty-one morphed faces were presented randomly for each trial. The participants were instructed to make a decision as to whether the stimuli were self-face or not. The responses and reaction times were recorded during the task. Results : There were no group differences of the morphing composition at the recognition start point as self-face. In patients with social phobia, the mean reaction time at the start point of recognizing as a self-face was 1,037.6 ms, which was significantly longer than that of normal controls (911.3 ms, p<0.05). Patients with social phobia showed a significant negative correlation between the mean reaction time and the severity of depression when the stimuli were recognized as a self-face (r=-0.421, p<0.05). Conclusion : A difficulty in attention rather than avoidance may be an important factor of face recognition in patients with social phobia. When considering self-face recognition in such patients, many factors, such as anxiety, depression, working memory and theory of mind, need to be considered.