Forensics and security at present often use low technological resources. Security measures often fail to update with the upcoming technology. This project is based on implementing an automatic face recognition of criminals or specific targets using machine-learning approach. Given a set of features to a Generative Adversarial Network(GAN), the algorithm generates an image of the target with the specified feature set. The input to the machine can either be a given set of features or a set of portraits varying from frontals to side profiles from which these features can be extracted. The accuracy of the system is directly proportional to the number of epochs trained in the network. The generated output image can vary from primitive, low resolution images to high quality images where features are more recognizable. This is then compared with a predefined database of existing people. Thus, the target can immediately be recognized with the generation of an artificial image with the given biometric feature set, which will be again compared by a discriminator network to check the true identity of the target.

It is important to secure the privacy of digital face image that are stored in central database. To impart privacy to such biometric face images, first the digital face image is split into two such that, each of it gives no idea of existence of the original face image and the original image can be retrieved only when both split images available. To achieve this, Visual Cryptography, Halftoning along with Digital Watermarking are employed. The method can be extended to preserve the privacy of a maximum of any n images.

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