High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks

Synthesizing face sketches from real photos and its inverse have many applications. However, photo/sketch synthesis remains a challenging problem due to the fact that photo and sketch have different characteristics. In this work, we consider this task as an image-to-image translation problem and explore the recently popular generative models (GANs) to generate high-quality realistic photos from sketches and sketches from photos. Recent GAN-based methods have shown promising results on image-to-image translation problems and photo-to-sketch synthesis in particular, however, they are known to have limited abilities in generating high-resolution realistic images. To this end, we propose a novel synthesis framework called Photo-Sketch Synthesis using Multi-Adversarial Networks, (PS2-MAN) that iteratively generates low resolution to high resolution images in an adversarial way. The hidden layers of the generator are supervised to first generate lower resolution images followed by implicit refinement in the network to generate higher resolution images. Furthermore, since photo-sketch synthesis is a coupled/paired translation problem, we leverage the pair information using CycleGAN framework. Both Image Quality Assessment (IQA) and Photo-Sketch Matching experiments are conducted to demonstrate the superior performance of our framework in comparison to existing state-of-the-art solutions. Code available at: https://github.com/lidan1/PhotoSketchMAN.Comment: Accepted by 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)(Oral

Fusion of intra- and inter-modality algorithms for face-sketch recognition

Identifying and apprehending suspects by matching sketches created from eyewitness and victim descriptions to mugshot photos is a slow process since law enforcement agencies lack automated methods to perform this task. This paper attempts to tackle this problem by combining Eigentransformation, a global intra-modality approach, with the Eigenpatches local intra-modality technique. These algorithms are then fused with an inter-modality method called Histogram of Averaged Orientation Gradients (HAOG). Simulation results reveal that the intra- and inter- modality algorithms considered in this work provide complementary information since not only does fusion of the global and local intra-modality methods yield better performance than either of the algorithms individually, but fusion with the inter-modality approach yields further improvement to achieve retrieval rates of 94.05% at Rank-100 on 420 photo-sketch pairs. This performance is achieved at Rank-25 when filtering of the gallery using demographic information is carried out.peer-reviewe

Markov Weight Fields for face sketch synthesis

Posters 1C - Vision for Graphics, Sensors, Medical, Vision for Robotics, ApplicationsGreat progress has been made in face sketch synthesis in recent years. State-of-the-art methods commonly apply a Markov Random Fields (MRF) model to select local sketch patches from a set of training data. Such methods, however, have two major drawbacks. Firstly, the MRF model used cannot synthesize new sketch patches. Secondly, the optimization problem in solving the MRF is NP-hard. In this paper, we propose a novel Markov Weight Fields (MWF) model that is capable of synthesizing new sketch patches. We formulate our model into a convex quadratic programming (QP) problem to which the optimal solution is guaranteed. Based on the Markov property of our model, we further propose a cascade decomposition method (CDM) for solving such a large scale QP problem efficiently. Experimental results on the CUHK face sketch database and celebrity photos show that our model outperforms the common MRF model used in other state-of-the-art methods. © 2012 IEEE.published_or_final_versionThe IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI., 16-21 June 2012. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2012, p. 1091-109

An Enhanced Computer Vision By Using MLP Approach To Forensic Face Sketch Recognition System‎

Technologies for suspect identification, detection, and recognition have become more critical in recent years. As a result, face recognition is an almost commonly used biometric technique. Investigators for Criminal and forensic computer vision researchers are interested in the human-recognized face sketches were drawn by artists. Hand-drawn face sketches are, according to studies, ‎still extremely rare, both in terms of artists and number of drawings, since forensic artists ‎prepare victim drawings based on descriptions were provided by eyewitnesses following an incident‎. Masks are sometimes used to conceal standard facial features such as noses, eyes, lips, and skin color, but face biometrics' outliner features are impossible to conceal. This paper concentrated on a particular face-geometrical feature that could calculate some similarity ratios between composite template photos and forensic sketches. Computer vision techniques such as Two-Dimensional Discrete Cosine Transform (2D-DCT) and the Self-Organizing Map (SOM) Neural Network are used to design a system for composite and forensic face sketch recognition

A survey on heterogeneous face recognition: Sketch, infra-red, 3D and low-resolution

Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets are being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research

Enhancement of the Adaptive Shape Variants Average Values by Using Eight Movement Directions for Multi-Features Detection of Facial Sketch

This paper aims to detect multi features of a facial sketch by using a novel approach. The detection of multi features of facial sketch has been conducted by several researchers, but they mainly considered frontal face sketches as object samples. In fact, the detection of multi features of facial sketch with certain angle is very important to assist police for describing the criminal's face, when criminal's face only appears on certain angle. Integration of the maximum line gradient value enhancement and the level set methods was implemented to detect facial features sketches with tilt angle to 15 degrees. However, these methods tend to move towards non features when there are a lot of graffiti around the shape. To overcome this weakness, the author proposes a novel approach to move the shape by adding a parameter to control the movement based on enhancement of the adaptive shape variants average values with 8 movement directions. The experimental results show that the proposed method can improve the detection accuracy up to 92.74%