Security surveillance are low-cost, ubiquitous systems, which are employed in smart cities around the world for threat monitoring and assessment. Manual observation, monitoring and tracking their population, detection, and reporting abnormal events in crowded places can be very challenging. Smart cities favor the use of sophisticated security systems, which can exceed human errors. Moreover, multi-view near-infrared surveillance systems pose challenges such as poor image quality, color discontinuity, occlusion, and image blur. Also, the performance of a recognition system depends on the specifications of the camera. All these distortions cause interference in feature extraction process in face or object classification systems. In this article, an intelligent multi-view image mosaicking algorithm, which combines near-infrared images captured from dozens of cameras/sensors is introduced. The presented system a) preserves facial features, b) avoids vertical banding (exposure variation), and c) solves color discontinuity aiding for face detection systems. The performance of this technique is tested against its ground truth, both subjectively and quantitatively. The quantitative analysis is performed using measures such as SSIM, MS-SSIM, AME, LogAMEE, and TDMEC.