Efficient Facial Recognition Authentication Using Edge and Density Variant Sketch Generator
Source abstract
Abstract
Image translation plays a significant role in realistic image synthesis, entertainment tasks such as editing and colorization, and security including personal identification. In Edge GAN, the major contribution is attribute guided vector that enables high visual quality content generation. This research study proposes automatic face image realism from freehand sketches based on Edge GAN. We propose a density variant image synthesis model, allowing the input sketch to encompass face features with minute details. The density level is projected into non-latent space, having a linear controlled function parameter. This assists the user to appropriately devise the variant densities of facial sketches and image synthesis. Composite data set of Large Scale CelebFaces Attributes (ClebA), Labelled Faces in the Wild (LFWH), Chinese University of Hong Kong (CHUK), and self-generated Asian images are used to evaluate the proposed approach. The solution is validated to have the capability for generating realistic face images through quantitative and qualitative results and human evaluation.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Face recognition and analysis · Generative Adversarial Networks and Image Synthesis · Face and Expression Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
Show source-provided references
1. Attribute2image: Conditional image generation from visual attributes
2. Deep face recognition
3. Patchmatch: A randomized correspondence algorithm for structural image editing10.1145/1531326.1531330 ↗
4. Sketch2photo: Internet image montage
5. Generative adversarial nets10.1145/3422622 ↗
6. Face recognition using eigenfaces
7. Face recognition using a fuzzy fisherface classifier10.1016/j.patcog.2005.01.018 ↗
8. Face description with local binary patterns: Application to face recognition10.1109/TPAMI.2006.244 ↗
9. A discriminative feature space for detecting and recognizing faces
10. Z. Lu, X. Jiang and A. ChiChung Kot, “Deep coupled resnet for low-resolution face recognition,” IEEE Signal Processing Letters, vol. 25, no. 4, pp. 526–530, 2018.10.1109/LSP.2018.2810121 ↗
11. Facenet: A unified embedding for face recognition and clustering
12. Labeled faces in the wild: A database for studying face recognition in unconstrained environments
13. Scene completion using millions of photographs10.1145/1276377.1276382 ↗
14. J. Lalonde, D. Hoiem, A. A. Efros, C. Rother, J. Winn et al., “Photo clip art,” ACM Transactions on Graphics (TOG), vol. 26, no. 3, pp. 3-es, 2007.10.1145/1276377.1276381 ↗
15. Mirrorgan: Learning text-to-image generation by re-description
16. A. Radford, L. Metz and S. Chintala, “Un-supervised representation learning with deep convolutional generative adversarial networks,” ArXiv Preprint, ArXiv: 1511.06434, 2015.
17. Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
18. Legacy photo editing with learned noise prior
19. Interactive sketch and fill: Multiclass sketch-to-image translation
20. Un-supervised image-to-image translation networks
21. S. Wang, L. Zhang, Y. Liang and Q. Pan, “Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis,” Computer Vision and Pattern Recognition (IEEE), pp. 2216–2223, 2012.
22. Image restoration using multiresolution texture synthesis and image in painting
23. A. V. Oord, N. Kalchbrenner, L. Espe-holt, O. Vinyals, A. Graves et al., “Conditional image generation with pixel cnn decoders,” ArXiv Preprint ArXiv: 1606.05328, pp. 1–13, 2016.
24. Facial attributes guided deep sketch-to-photo synthesis
25. Learning to generate chairs with convolutional neural networks
26. View synthesis by appearance flow
27. C. K. Sønderby, J. Caballero, L. Theis, W. Shi and F. Huszár, “Amortised map inference for image super-resolution,” ArXiv Preprint ArXiv: 1610.04490, 2016.
28. Face aging with conditional generative adversarial networks
29. Pixel recurrent neural networks
30. S. E. Reed, Z. Akata, S. Mohan, S. Tenka, B. Schiele et al., “Learning what and where to draw,” ArXiv Preprint ArXiv: 1606.05328, pp. 1–9, 2016.
31. Image-to-image translation with conditional adversarial networks
32. PredGAN: A deep multi-scale video prediction framework for detecting anomalies in videos
33. Precomputed real-time texture synthesis with markovian generative adversarial networks
34. Generative adversarial text to image synthesis
35. Unsuper-vised cross-domain image generation
36. Dual learning for machine translation
37. Scribbler: Controlling deep image synthesis with sketch and color