Pose-Invariant Face Recognition (PIFR) has been a serious challenge in the general field of face recognition (FR). The performance of face recognition algorithms deteriorate due to various degradations such as pose, illuminaton, occlusions, blur, noise, aliasing, etc. In this paper, we deal with the problem of 3D pose variation of a face. for that we design and propose PosIX Generative Adversarial Network (PosIX-GAN) that has been trained to generate a set of nice (high quality) face images with 9 different pose variations, when provided with a face image in any arbitrary pose as input. The discriminator of the GAN has also been trained to perform the task of face recognition along with the job of discriminating between real and generated (fake) images. Results when evaluated using two benchmark datasets, reveal the superior performance of PosIX-GAN over state-of-the-art shallow as well as deep learning methods. © Springer Nature Switzerland AG 2019.