![]() Users are provided with several functions to interactively breed and edit faces. Specifically, CG-GAN utilizes the generator network of a pg-GAN to create high-resolution human faces. The novel approach, Composite Generating GAN (CG-GAN), applies generative and evolutionary computation to allow casual users to easily create facial composites. In this paper, we improve the efficiency of composite creation by removing the reliance on expert knowledge and letting the system learn to represent faces from examples. Many digital systems are available for the creation of such composites but are either unable to reproduce features unless previously designed or do not allow holistic changes to the image. We have also shown the summary of the performance of representative state-of-the-art methods.įacial composites are graphical representations of an eyewitness's memory of a face. The review focuses firstly on the traditional methods and their categorization also shows the evolution of suspect face retrieval approaches over the years. In this paper, we have provided an extensive review of the available methods for suspect face retrieval using visual and linguistic information. Recently, linguistic information is also utilized for suspect face retrieval. In the recent past, lots of sketch-to-photograph retrieval methods are proposed by many researchers however, they have ignored the uncertainty of facial attributes for suspect face retrieval. In a forensic sketch, the facial description depends on the memory of the eyewitness therefore, there is uncertainty in facial attributes. Forensic sketches are normally developed by the sketch artist based on verbal details provided by an eyewitness about the suspect. ![]() Law enforcement agencies use face as a key point to identify the suspect involved in unlawful activities. Faces are the most common biometric used for the identification of a person. ![]()
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