Pembangkitan Citra Wajah dari Sketch Wajah menggunakan CycleGAN

Penggunaan sketsa wajah merupakan alat bantu yang digunakan lembaga penegak hukum dalam melakukan proses identifikasi tersangka tindak kriminal. Sketsa wajah digunakan ketika tidak terdapat foto dari tersangka tindak kriminal di tempat kejadian perkara. Sketsa wajah digunakan dalam proses identifikasi mugshot pada database dengan menggunakan sistem face recognition, dikarenakan sketsa wajah memiliki modalitas yang berbeda dengan citra wajah seperti halnya tekstur wajah, maka dibangkitkanlah citra wajah baru dari input sketsa wajah yang dimiliki sehingga dapat memiliki tekstur yang dapat menyerupai citra wajah. CycleGAN merupakan metode yang digunakan dalam melakukan tugas imageto-image translation, metode tersebut dapat digunakan dalam melakukan style transfer. Oleh karena itu, dalam penelitian tugas akhir ini dikembangkan sebuah model yang berfungsi untuk membangkitkan citra wajah dari sketsa wajah sehingga dapat mengolah sketsa wajah menjadi citra wajah yang memilki tekstur wajah

Pembangkitan Citra Wajah dari Sketch Wajah menggunakan CycleGAN

Penggunaan sketsa wajah merupakan alat bantu yang digunakan lembaga penegak hukum dalam melakukan proses identifikasi tersangka tindak kriminal. Sketsa wajah digunakan ketika tidak terdapat foto dari tersangka tindak kriminal di tempat kejadian perkara. Sketsa wajah digunakan dalam proses identifikasi mugshot pada database dengan menggunakan sistem face recognition, dikarenakan sketsa wajah memiliki modalitas yang berbeda dengan citra wajah seperti halnya tekstur wajah, maka dibangkitkanlah citra wajah baru dari input sketsa wajah yang dimiliki sehingga dapat memiliki tekstur yang dapat menyerupai citra wajah. CycleGAN merupakan metode yang digunakan dalam melakukan tugas imageto-image translation, metode tersebut dapat digunakan dalam melakukan style transfer. Oleh karena itu, dalam penelitian tugas akhir ini dikembangkan sebuah model yang berfungsi untuk membangkitkan citra wajah dari sketsa wajah sehingga dapat mengolah sketsa wajah menjadi citra wajah yang memilki tekstur wajah

Improved Sketch-to-Photo Generation Using Filter Aided Generative Adversarial Network

Generating a photographic face image from given input sketch is most challenging task in computer vision. Mainly the sketches drawn by sketch artist used in human identification. Sketch to photo synthesis is very important applications in law enforcement as well as character design, educational training. In recent years Generative Adversarial Network (GAN) shows excellent performance on sketch to photo synthesis problem.  Quality of hand drawn sketches affects the quality generated photo. It might be possible that while handling the hand drawn sketches, accidently by touching the user hand on pencil sketch or similar activities causes noise in given sketch. Likewise different styles like shading, darkness of pencil used by sketch artist may cause unnecessary noise in sketches. In recent year many sketches to photo synthesis methods are proposed, but they are mainly focused on network architecture to get better performance. In this paper we proposed Filter-aided GAN framework to remove such noise while synthesizing photo images from hand drawn sketches. Here we implement and compare different filtering methods with GAN.  Quantitative and qualitative result shows that proposed Filter-aided GAN generate the photo images which are visually pleasant and closer to ground truth image

Identity-preserving Face Recovery from Portraits

Recovering the latent photorealistic faces from their artistic portraits aids human perception and facial analysis. However, a recovery process that can preserve identity is challenging because the fine details of real faces can be distorted or lost in stylized images. In this paper, we present a new Identity-preserving Face Recovery from Portraits (IFRP) to recover latent photorealistic faces from unaligned stylized portraits. Our IFRP method consists of two components: Style Removal Network (SRN) and Discriminative Network (DN). The SRN is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. By embedding spatial transformer networks into the SRN, our method can compensate for misalignments of stylized faces automatically and output aligned realistic face images. The role of the DN is to enforce recovered faces to be similar to authentic faces. To ensure the identity preservation, we promote the recovered and ground-truth faces to share similar visual features via a distance measure which compares features of recovered and ground-truth faces extracted from a pre-trained VGG network. We evaluate our method on a large-scale synthesized dataset of real and stylized face pairs and attain state of the art results. In addition, our method can recover photorealistic faces from previously unseen stylized portraits, original paintings and human-drawn sketches

Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches

Image generation task has received increasing attention because of its wide application in security and entertainment. Sketch-based face generation brings more fun and better quality of image generation due to supervised interaction. However, When a sketch poorly aligned with the true face is given as input, existing supervised image-to-image translation methods often cannot generate acceptable photo-realistic face images. To address this problem, in this paper we propose Cali-Sketch, a poorly-drawn-sketch to photo-realistic-image generation method. Cali-Sketch explicitly models stroke calibration and image generation using two constituent networks: a Stroke Calibration Network (SCN), which calibrates strokes of facial features and enriches facial details while preserving the original intent features; and an Image Synthesis Network (ISN), which translates the calibrated and enriched sketches to photo-realistic face images. In this way, we manage to decouple a difficult cross-domain translation problem into two easier steps. Extensive experiments verify that the face photos generated by Cali-Sketch are both photo-realistic and faithful to the input sketches, compared with state-of-the-art methodsComment: 10 pages, 12 figure