Background: Face recognition (FR) is a promising biometric trait widely used for authentication in several applications like finance, military, security, surveillance and so on in daily life. Deep learning involves several processing layers for learning data representations with feature extraction at multiple levels. Hence, deep FR techniques with hierarchical architecture which puts pixels together to represent invariant face has drastically improved the recognition performance and promoted real-time applications successfully. Objective: In this research, an Improved FireFly (IFF) algorithm is developed to recognize face whose performance is estimated by the integration of RNN network. Method: Based on the selected dataset, RNN involved in analysis of facial features with inclusion of improved firefly algorithm (RnnIFF). Result: The results stated that proposed approach provides higher value of accuracy, precision and sensitivity expressing 90%, 90% and 91% respectively. Also, Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) is evaluated and comparatively examined with existing techniques. The simulation results illustrated that proposed RnnIFF exhibits significant performance for recognition of faces.