Localin Reshuffle Net: Toward Naturally and Efficiently Facial Image Blending

2021 ◽  
pp. 206-222
Author(s):  
Chengyao Zheng ◽  
Siyu Xia ◽  
Joseph Robinson ◽  
Changsheng Lu ◽  
Wayne Wu ◽  
...  
Keyword(s):  
Facial Image ◽  
Image Blending

Facial Image Super-Resolution Reconstruction Based on Separated Frequency Components

2013 ◽  
Vol E96.A (6) ◽  
pp. 1315-1322 ◽  
Author(s):  
Hyunduk KIM ◽  
Sang-Heon LEE ◽  
Myoung-Kyu SOHN ◽  
Dong-Ju KIM ◽  
Byungmin KIM
Keyword(s):  
Super Resolution ◽  
Facial Image ◽  
Frequency Components ◽  
Image Super Resolution

Analysis of statistics for facial image color between ages

2017 ◽  
Author(s):  
Eunjoo Kim
Keyword(s):  
Facial Image

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

scholarly journals Maximizing Image Information Using Multi-Chimera Transform Applied on Face Biometric Modality

Information ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 115
Author(s):  
Ahmad Saeed Mohammad ◽  
Dhafer Zaghar ◽  
Walaa Khalaf
Keyword(s):  
Mobile Technology ◽  
Data Reduction ◽  
Research Field ◽  
File Size ◽  
Facial Image ◽  
Information Preservation ◽  
Media Data ◽  
Reconstructed Data ◽  
Face Biometric ◽  
High Information

With the development of mobile technology, the usage of media data has increased dramatically. Therefore, data reduction represents a research field to maintain valuable information. In this paper, a new scheme called Multi Chimera Transform (MCT) based on data reduction with high information preservation, which aims to improve the reconstructed data by producing three parameters from each 16×16 block of data, is proposed. MCT is a 2D transform that depends on constructing a codebook of 256 picked blocks from some selected images which have a low similarity. The proposed transformation was applied on solid and soft biometric modalities of AR database, giving high information preservation with small resulted file size. The proposed method produced outstanding performance compared with KLT and WT in terms of SSIM and PSNR. The highest SSIM was 0.87 for the proposed scheme MCT of the full image of AR database, while the existed method KLT and WT had 0.81 and 0.68, respectively. In addition, the highest PSNR was 27.23 dB for the proposed scheme on warp facial image of AR database, while the existed methods KLT and WT had 24.70 dB and 21.79 dB, respectively.

An analysis of facial expression recognition under partial facial image occlusion

2008 ◽  
Vol 26 (7) ◽  
pp. 1052-1067 ◽  
Author(s):  
Irene Kotsia ◽  
Ioan Buciu ◽  
Ioannis Pitas
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Facial Image

Facial Image Super Resolution on 3 Architectures of Generative Adversarial Network

2020 ◽  
Author(s):  
M. Alfin N. Kemas ◽  
Ariq Suryo Hadi P. ◽  
Yudi Widhiyasana ◽  
Nurjannah Syakrani
Keyword(s):  
Super Resolution ◽  
Facial Image ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Image Super Resolution

Curvature based normalized 3D component facial image recognition using fuzzy integral

2008 ◽  
Vol 205 (2) ◽  
pp. 815-823 ◽  
Author(s):  
Yeunghak Lee ◽  
David Marshall
Keyword(s):  
Image Recognition ◽  
Fuzzy Integral ◽  
Facial Image

scholarly journals Generation of an EDS Key Based on a Graphic Image of a Subject’s Face Using the RC4 Algorithm

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 19
Author(s):  
Alexey Semenkov ◽  
Dmitry Bragin ◽  
Yakov Usoltsev ◽  
Anton Konev ◽  
Evgeny Kostuchenko
Keyword(s):  
Image Recognition ◽  
Random Number ◽  
Statistical Test ◽  
The Other ◽  
Test Results ◽  
Random Generation ◽  
Facial Image ◽  
Random Number Generators ◽  
Graphic Image ◽  
High Level

Modern facial recognition algorithms make it possible to identify system users by their appearance with a high level of accuracy. In such cases, an image of the user’s face is converted to parameters that later are used in a recognition process. On the other hand, the obtained parameters can be used as data for pseudo-random number generators. However, the closeness of the sequence generated by such a generator to a truly random one is questionable. This paper proposes a system which is able to authenticate users by their face, and generate pseudo-random values based on the facial image that will later serve to generate an encryption key. The generator of a random value was tested with the NIST Statistical Test Suite. The subsystem of image recognition was also tested under various conditions of taking the image. The test results of the random value generator show a satisfactory level of randomness, i.e., an average of 0.47 random generation (NIST test), with 95% accuracy of the system as a whole.

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