Face Sketch Synthesis via Sparse Representation-Based Greedy Search

   December 11, 2017    Comments Off on Face Sketch Synthesis via Sparse Representation-Based Greedy Search    Posted in: IEEE 2017, MATLAB

Face Sketch Synthesis via Sparse Representation-Based Greedy Search

ABSTRACT:

Face sketch synthesis has wide applications in digital entertainment and law enforcement. Although there is much research on face sketch synthesis, most existing algorithms cannot handle some nonfacial factors, such as hair style, hairpins, and glasses if these factors are excluded in the training set. In addition, previous methods only work on well controlled conditions and fail on images with different backgrounds and sizes as the training set. To this end, this paper presents a novel method that combines both the similarity between different image patches and prior knowledge to synthesize face sketches. Given training photo-sketch pairs, the proposed method learns a photo patch feature dictionary from the training photo patches and replaces the photo patches with their sparse coefficients during the searching process. For a test photo patch, we first obtain its sparse coefficient via the learnt dictionary and then search its nearest neighbors (candidate patches) in the whole training photo patches with sparse coefficients. After purifying the nearest neighbors with prior knowledge, the final sketch corresponding to the test photo can be obtained by Bayesian inference. The contributions of this paper are as follows: 1) we relax the nearest neighbor search area from local region to the whole image without too much time consuming and 2) our method can produce non-facial factors that are not contained in the training set and is robust against image backgrounds and can even ignore the alignment and image size aspects of test photos. Our experimental results show that the proposed method outperforms several state of-the-arts in terms of perceptual and objective metrics.

EXISTING SYSTEM:

  • In Existing methods about face sketch synthesis could be sorted into three categories: the subspace learning framework, the sparse representation based approaches and the Bayesian inference framework.
  • Tang and Wang proposed principle component analysis based methods to face sketch synthesis. These methods assumed that the mapping between a photo and its corresponding sketch was a linear transformation. However, due to the complexity of human face image, the relationship between face photos and face sketches may preferably be estimated as a nonlinear function.
  • Liu et al. adopted the idea of a locally linear embedding to model the nonlinear process of face sketch synthesis. It is obvious that the LLE-based method is insufficient to simulate the real nonlinear relationship between photos and sketches. Further, these methods above need a great many of training samples.

DISADVANTAGES OF EXISTING SYSTEM:

  • Existing algorithms cannot handle some non-facial factors, such as hair style, hairpins, and glasses if these factors are excluded in the training set.
  • In addition, previous methods only work on well controlled conditions and fail on images with different backgrounds and sizes as the training set.

 

PROPOSED SYSTEM:

  • In this paper, we developed a novel approach to face sketch synthesis by incorporating both the similarity between different image patches and prior knowledge. Greedy search based on sparse coefficients is adopted to measure the similarity between the test photo patches and the training photo patches. Intensity and gradient priors are employed to compensate the greedy search stage.
  • Instead of directly employing raw test photo patches to search for nearest photo patches in the training set, which is time consuming and requires huge computational memory, we adopt sparse coefficients to replace the raw image patches to overcome the aforementioned limitations. Moreover, by sparse coefficients, we can expand the search range into the whole image, which is impractical for existing patch level based methods due to the computational complexity.
  • In our method, the faces to be synthesized could possess some non-facial factors, such as glasses and mustache etc.. The test photo can also be in diverse poses with different backgrounds and sizes. The proposed method can even deal with images including multiple faces.

ADVANTAGES OF PROPOSED SYSTEM:

  • We relaxed the search range from local area to the whole image via sparse coding without increasing the computational cost too much.
  • The proposed face sketch synthesis method could handle some non-facial factors, such as hair style, hairpins and glasses excluded in the training set and different kinds of test photos ignoring image backgrounds, image size and face posture etc..

 

 

 

 

SYSTEM ARCHITECTURE:

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

 

  • System : Pentium IV 2.4 GHz.
  • Hard Disk : 40 GB.
  • Floppy Drive : 44 Mb.
  • Monitor : 15 VGA Colour.
  • Mouse :
  • Ram : 512 Mb.

 

SOFTWARE REQUIREMENTS:

 

  • Operating system : Windows XP/7.
  • Coding Language : MATLAB
  • Tool : MATLAB R2013A

 

REFERENCE:

Shengchuan Zhang, Xinbo Gao, Senior Member, IEEE, Nannan Wang, Jie Li, and Mingjin Zhang, “Face Sketch Synthesis via Sparse Representation-Based Greedy Search”, IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 24, NO. 8, AUGUST 2015.

 

 

  

Comments are closed.