Review on Deep Face Drawing on basis of Sketch using CNN

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

In forensic science, it is seen that hand-drawn face sketches are still very limited and time consuming when it comes to using them with the latest technologies used for recognition and identification of criminals. In this paper, we present a standalone application which would allow users to create composites face sketch of the suspect without the help of forensic artists using drag and drop feature in the application and can automatically match the drawn composite face sketch with the police database much faster and efficiently using deep learning and cloud infrastructure.

In recent years, face photo-sketch synthesis has considerably improved due to the development of deep convolutional neural networks. The technique known as FACE photo-sketch synthesis, which aims to produce a face sketch from a face photo and vice-versa has drawn a lot of attention and found useful in both law enforcement and digital entertainment. Facial image sketches are frequently created using applications for social networks and short videos for entertainment purposes. This problem has been addressed in a variety of ways, all of which have proven successful. However, there are still problems that need to be fixed in apps that are used in the real world. Face recognition (FR) makes it nearly impossible to directly utilize a sketch to search for the appropriate photo in the gallery due to the glaring contrasts between the two face domains. Transferring the faces into the same domain and using an off-the-shelf high-performance face verifier, which demands that the produced faces keep their distinguishing characteristics, is one way for identifying the target person. Numerous Face Recognition systems have been developed in recent years, but these systems still need to be improved in a number of aspects, including availability, performance, efficiency, and accuracy. Here, a comparison of various methods being developed to accurately detect the Face is being done.

Intelligent Automation & Soft Computing

Generating an RGB image from a sketch is a challenging and interesting topic. This paper proposes a method to transform a face sketch into a color image based on generation confrontation network and edge optimization. A neural network model based on Generative Adversarial Networks for transferring sketch to RGB image is designed. The face sketch and its RGB image is taken as the training data set. The human face sketch is transformed into an RGB image by the training method of generative adversarial networks confrontation. Aiming to generate a better result especially in edge, an improved loss function based on edge optimization is proposed. The experimental results show that the clarity of the output image, the maintenance of facial features, and the color processing of the image are enhanced best by the image translation model based on the generative adversarial network. Finally, the results are compared with other existing methods. Analyzing the experimental results shows that the color face image generated by our method is closer to the target image, and has achieved a better performance in term of Structural Similarity (SSIM).

ITM Web of Conferences

A criminal can be quickly identified and prosecuted using a face sketch based on an eyewitness description . Several applications for converting hand-drawn face drawings and using them to automatically identify and recognize the suspect from the police database have been proposed in the past, but the existing system dealt with some drawbacks. It featured a lot of flaws, including as a limited facial features kit and a cartoonish feel to the constructed suspect face, which made it much more difficult to use these apps and acquire the results and efficiency that were required. In this paper, we present a stand- alone tool that allows users to create composite face sketches of suspects without the need for forensic artists. The application offers a drag-and-drop feature that can match the produced composite facial sketch with the criminal database in real time. This can be done considerably more rapidly and efficiently using deep learning and cloud infrastructure.

International Journal of Advanced Science and Technology, 2020

Each individual has specific identity, which can be identified through faces. This system is based on two techniques-Face detection and Face recognition. This process is performed to detect the faces using inbuilt camera inside room and Face recognition is execute on live obtained images to detect whether the required faces are present in the captured video or not. The system first detects the face and compared with dataset to recognize the face. Face recognition task is to identify the individual face from captured multiple faces. After separation of face, it will check with stored image properties. A face recognition system is done with help of biometric data processs; its applicability is available on fingerprint scanning, signature, etc. We proposed deep learning algorithm Convolutional Neural Network (CNN), used for image identification and classification purpose. Face recognition system plays a vital role in recognizing the faces then to perform some real time computation in the system. It is mainly used in the biometric system. These systems have higher accuracy compared to traditional techniques.

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020

Facial sketches drawn by artists are widely used for visual identification applications and mostly by law enforcement agencies, but the quality of these sketches depend on the ability of the artist to clearly replicate all the key facial features that could aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. In this work, we propose a novel approach that adopts a generative adversarial network that synthesizes a single sketch into multiple synthetic images with unique attributes like hair color, sex, etc. We incorporate a hybrid discriminator which performs attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network and an identity preserving network that maintains the identity of the synthesised image throughout the training process. Our approach is aimed at improving the visual appeal of the synthesised images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesised image. We synthesised sketches using XDOG filter for the CelebA, WVU Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results are impressive compared to current state of the art.

Face sketch synthesis is a process of creating a sketch from available face photograph. This process is widely used in law enforcement and digital entertainment. If some factors except the facial are not present in training set, but they are present in photograph like hairpin, hair style, glasses, then it becomes more difficult to handle. And existing methods fails if the photographs are of different background and size. This results to poor quality of sketches. For that purpose different approaches are proposed. And this paper contains a review of all existing approaches were used for face sketch synthesis.

2019

Facial sketches are widely used in judicial and legal proceedings. Law enforcers use facial sketches to help them with the visual aspects of the case, using witness descriptions and video footage. However, drawing forensic sketches by hand is a time-consuming procedure and a situation may arise where the authorities have less time in hand to solve a case. The present research work aims to create a basic model which can generate facial images from a given set of input features; similar to what a forensic artist does, thus, enabling a faster and efficient sketching procedure. In this work, a category of generative algorithms, called Generative Adversarial Networks has been used to build this model. To train this model, a dataset of anime girls has been used and thus it can only generate the same, making sure that the generated image contains the input features.

International Journal of Electrical and Computer Engineering (IJECE), 2024

This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketchphoto transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), 2018

In this paper, we present a deep coupled framework to address the problem of matching sketch image against a gallery of mugshots. Face sketches have the essential information about the spatial topology and geometric details of faces while missing some important facial attributes such as ethnicity, hair, eye, and skin color. We propose a coupled deep neural network architecture which utilizes facial attributes in order to improve the sketch-photo recognition performance. The proposed Attribute-Assisted Deep Convolutional Neural Network (AADCNN) method exploits the facial attributes and leverages the loss functions from the facial attributes identification and face verification tasks in order to learn rich discriminative features in a common embedding subspace. The facial attribute identification task increases the inter-personal variations by pushing apart the embedded features extracted from individuals with different facial attributes, while the verification task reduces the intra-personal variations by pulling together all the features that are related to one person. The learned discriminative features can be well generalized to new identities not seen in the training data. The proposed architecture is able to make full use of the sketch and complementary facial attribute information to train a deep model compared to the conventional sketch-photo recognition methods. Extensive experiments are performed on composite (E-PRIP) and semi-forensic (IIIT-D semi-forensic) datasets. The results show the superiority of our method compared to the stateof-the-art models in sketch-photo recognition algorithms.