Two dimensional synthetic face generation and verification using set estimation technique

In this paper set estimation technique is applied for generation of 2D face images. The synthesis is done on the basis of inheriting features from inter and intra face classes in face space. Face images without artifacts and expressions are transformed to images with artifacts and expressions with the help of the developed methods. Most of the test images are generated using the proposed method. The measured PSNR values for the generated faces with respect to the training faces reflect the well accepted quality of the generated images. The generated faces are also classified properly to their respective face classes using nearest neighbor classifier. Validation of the method is demonstrated on AR and FIA datasets. Classification accuracy is increased when the new generated faces are added to the training set.

► Set estimation technique is applied to generate synthetic 2D face images. ► Synthesis is done on the basis of inheriting features from inter and intra face classes in face space. ► Validation of the method is experimented on AR and FIA datasets. ► Images without artifacts and expressions are transformed to images with artifacts and expressions. ► Classification accuracy is also found to increase when the new faces are added to the training set.