Information technology an application to human face sketch synthesis and recognition

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1, 2 Department of Information Technology,

Bharati Vidyapeeth Deemed University

College Of Engineering, Pune-46

ABSTRACT :To synthesize sketch/photo images, the face region is divided into overlapping patches for learning. The size of the patches decides the scale of local face structures to be learned. From a training set which contains photo-sketch pairs, the joint photo-sketch model is learned at multiple scales using a multiscale MRF model. By transforming a face photo to a sketch (or transforming a sketch to a photo), the difference between photos and sketches is significantly reduced, thus allowing effective matching between the two in face sketch recognition. After the photo-sketch transformation, in principle, most of the proposed face photo recognition approaches can be applied to face sketch recognition in a straightforward way Extensive experiments are conducted on a face sketch database including 606 faces. Biometrics is a form of bioinformatics that uses biological properties to identify individuals. Examples of biometrics are fingerprinting, facial recognition, iris scanning, signature authentication, and voice recognition and hand geometry. Facial recognition is simply using characteristics of the face to identify an individual.
Keywords: Filtering Face Recognition, Face Sketch Synthesis, Face Sketch Recognition, Multiscale Markov Random Field.


Face Photo-Sketch Synthesis and Recognition is an Online System, that is a complete software solution for efficiently managing the huge data generated in police department and take action on the same. It helps in fighting crime and criminals in a more responsive, quick and proactive way, by engaging public, NGOs, police and government agencies. Over the years, when one is working with the police department, it is natural to feel that the flow of required information and data should be flawless, smooth and correct An important application of face recognition is to assist law enforcement. Automatic retrieval of photos of suspects from the police mug shot database can help the police narrow down potential suspects quickly. However, in most cases, the photo image of a suspect is not available.

The best substitute is often a sketch drawing based on the recollection of an eyewitness. Therefore, automatically searching through a photo database using a sketch drawing becomes important. It can not only help police locate a group of potential suspects, but also help the witness and the artist modify the sketch drawing of the suspect interactively based on similar photos retrieved [1], [7]. However, due to the great difference between sketches and photos and the unknown psychological mechanism of sketch generation, face sketch recognition is much harder than normal face recognition based on photo images. It is difficult to match photos and sketches in two different modalities. One way to solve this problem is to first transform face photos into sketch drawings and then match a query sketch with the synthesized sketches in the same modality, or first transform a query sketch into a photo image and then match the synthesized photo with real photos in the gallery. Face sketch/photo synthesis not only helps face sketch recognition, but also has many other useful applications for digital entertainment [8], [9]. In this paper, we will study these two interesting and related problems: face sketch/photo synthesis and face sketch recognition. Artists have a fascinating ability to capture the most distinctive characteristics of human faces and depict them on sketches. Although sketches are very different from photos in style and appearance, we often can easily recognize a person from his sketch. How to synthesize face sketches from photos by a computer is an interesting problem. The psychological mechanism of sketch generation is difficult to be expressed precisely by rules or grammar. The difference between sketches and photos mainly exists in two aspects: texture and shape [2]. The patches drawn by pencil on paper have different texture compared to human skin captured on a photo. In order to convey the 3D shading information, some shadow texture is often added to sketches by artists. For shape, a sketch exaggerates some distinctive facial features just like a caricature, and thus involves shape deformation. For example, if a face has a big nose in a photo, the nose drawn in the sketch will be even bigger.

Increase citizen satisfaction by providing searchable, sort able crime lists and maps available. No additional IT resources required to manage the system.No development costs. No additional report writing or data management required. A smart environment is one that is able to identify people, interpret their actions, and react appropriately. Thus, one of the most important building blocks of smart environments is a person identification system. Face recognition devices are ideal for such systems, since they have recently become fast, cheap, unobtrusive, and, when combined with voice-recognition, are very robust against changes in the environment. Moreover, since humans primarily recognize each other by their faces and voices, they feel comfortable interacting with an environment that does the same.

So to cope up with this situation we got an idea about this project. We planned to build out project using JAVA so that applications are run on different platforms In this our project we are having much functionality implemented in simpler ways in order to file or register the complaint faster and in much simpler manner.


In psychology study, researchers have long been using various face drawings, especially line drawings of faces, to investigate face recognition by the human visual system [10], [11], [12], [13], [14]. Human beings can recognize caricatures quite well, which is a special kind of line drawings of faces, with particular details of a face accentuated, compared with the ability to recognize face photos. Presumably, the details which get accentuated in caricaturing are those which are characteristics of that individual. Someone even question whether caricatures are in any way better representations than natural images, since caricatures may contain not only the essential minimum of information but also some kind of “superfidelity” due to the accentuated structures [10]. It is also shown that computer-drawn “cartoons” with edges, pigmentation, and shading of the original image can be well recognized by human beings. Some computer-based sketch synthesis systems have been proposed in recent years. Most of them have the linedrawing output without much sketch texture which is useful to convey 3D shading information. In [8], [9], face shape was extracted from a photo and exaggerated by some rules to make the result more similar to a sketch in shape. They were not based on learning. Freeman et al. [15] proposed an example-based system which translated a line drawing into different styles. Chen et al. [16] proposed an example-based face cartoon generation system. It was also limited to the line drawings and required the perfect match between photos and line drawings in shape. These systems relied on the extraction of face shape using face alignment algorithms such as Active Appearance Model (AAM) [17]. These line drawings are less expressive than the sketches with shading texture. In this paper, we work on sketches with shading texture.

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