Computer vision systems open a new challenge to recognize human faces under varied poses in similar capacity and capability as human-beings perform naturally. For surveillance applications, pose-invariant face recognition (PIFR) will become a major break-through by presenting the solution of this unique challenge. In recent decade, several techniques are presented to address this challenge over well-known data-sets. These efforts are divided chronologically into seven different approaches say geometric, statistical, holistic, template, supervised learning, unsupervised learning and deep learning. Among these deep learning techniques have shown more promising results and have gained attention for future research. By reviewing PIFR, it is historically divided into five eras based on 160 referred papers and their cumulative citations.