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http://dx.doi.org/10.9717/kmms.2022.25.2.176

A New Face Morphing Method using Texture Feature-based Control Point Selection Algorithm and Parallel Deep Convolutional Neural Network  

Park, Jin Hyeok (Dept. of IT Convergence and Application Engineering, PuKyong National University)
Khan, Rafiul Hasan (Dept. of IT Convergence and Application Engineering, PuKyong National University)
Lim, Seon-Ja (Dept. of IT Convergence and Application Engineering, PuKyong National University)
Lee, Suk-Hwan (Dept. of Computer Engineering, Dong-A University)
Kwon, Ki-Ryong (Dept. of IT Convergence and Application Engineering, PuKyong National University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.2, 2022 , pp. 176-188 More about this Journal
Abstract
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.
Keywords
Anthropomorphism; Face Morphing; Texture Features; Parallel Deep Convolutional Neural Network;
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