{"references": ["1.\tD. Kang, H. Han, A. K. Jain, and S.W. Lee, \"Nighttime Face Recognition at Large Standoff: Cross Distance and Cross Spectral Matching\", Pattern Recognition, Vol. 47, No. 12, pp. 3750-3766, 2014. 2.\tH. Maeng, S. Liao, D. Kang, S.W. Lee, and A. K. Jain, \"Nighttime Face Recognition at Long Distance: Cross distance and Cross-spectral Matching\", ACCV, Daejeon, Korea, Nov. 59, 2012. 3.\tBan, K.D., Lee, J., Kim, D., Kim, J. and Chung, Y.K., \"Tiny and blurred face alignment for long distance face recognition\", ETRI Journal, Vol. 33, No. 2, pp.251-258, 2011. 4.\tSandeep Kumar, Sukhwinder Singh, and Jagdish Kumar, \"A Study on Face Recognition Techniques with Age and Gender Classification\", In IEEE International Conference on Computing, Communication and Automation (ICCCA), 5th-6th May 2017. 5.\tSandeep Kumar, Sukhwinder Singh, and Jagdish Kumar, \"A Comparative Study on Face Spoofing Attacks\", In IEEE International Conference on Computing, Communication and Automation (ICCCA), 5th-6th May 2017. 6.\tAbbad, A., Abbad, K. and Tairi, H., \"March. Face Recognition Based on City-Block and Mahalanobis Cosine Distance\", In IEEE 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), pp. 112-114, 2016. 7.\tMoon, H.M., and Pan, S.B., \"The LDA-based face recognition at a distance using multiple distance image\", In IEEE Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 249-255, July-2013. 8.\tYap, Moi Hoon, Hassan Ugail, Reyer Zwiggelaar, Bashar Rajoub, Victoria Doherty, Stephanie Appleyard, and Gemma Hurdy. \"A short review of methods for face detection and multifractal analysis.\" The IEEE International Conference on Cyber Worlds, pp. 231-236, 2009. 9.\tRaghavendra, R., Raja, K.B., Yang, B. and Busch, C., \"Multi-face Recognition at a Distance Using Light-Field Camera\", In IEEE Ninth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 346-349, October-2013. 10.\tMalkauthekar, M.D., \"Analysis of Euclidean distance and Manhattan distance measure in Face Recognition\", 2013. 11.\tMoon, H.M., Choi, D., Kim, P. and Pan, S.B., \"LDA-based face recognition using multiple distance training face images with low user cooperation\", In IEEE International Conference on Consumer Electronics (ICCE), pp. 7-8, January-2015. 12.\tZhang, X., Peng, M., and Chen, T., \"Face recognition from near-infrared images with convolutional neural network\", In 8th International Conference on Wireless Communications & Signal Processing (WCSP), pp. 1-5, October-2016. 13.\tMaeng, H., Choi, H.C., Park, U., Lee, S.W. and Jain, A.K., \"Near-infrared face recognition at a distance\", In IEEE International Joint Conference on Biometrics (IJCB), pp. 1-7, October-2011. 14.\tKim, S. and Yang, S., \"Environmental illumination invariant face recognition using near infrared imaging system\", In IEEE 9th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 87-92, September-2015."]}

Nowadays near-infrared face recognition technology with light intensity and face recognition at a distance without the cooperation of users has gained wide attention toward these surveillance systems. Such type of environmental illumination i.e. near-infrared and face recognition at a distance in both daytime and night time can degrade the performance of surveillance systems. In the last decade, the whole biometric communities have worked on challenging tasks to develop a more accurate protection method against Near-Infrared or Long Distance database at distances of 1 meters, 60 meters, 100 meters, and 150 meters, with both daytime and nighttime images. This paper presents an improved technique of fdlibmex algorithm. The paper presents a detailed study and results of environmental illumination for face recognition. This paper also provides future directions for further research.