![]() Ranjan R, Patel VM, Chellappa R (2015) A deep pyramid deformable part model for face detection. Nasir AFA, Ghani ASA, Zakaria MA, Majeed APPA, Ibrahim AN (2019) Automated face detection using skin colour segmentation and Viola–Jones algorithm. Kumar A, Kaur A, Kumar M (2019) Face detection techniques: a review. Khandait S, Khandait PD, Thool R (2009) An efficient approach to facial feature detection for expression recognition. Proceedings of the 12th IEEE International Conference on Automatic Face & Gesture Recognition, 650-657 Jiang H, Miller EL (2017) Face detection with the faster R-CNN. J Inf Hiding Multimedia Signal Process 2(2):123–132 Hu WC, Yang CY, Huang DY, Huang CH (2011) Feature-based face detection against skin-color like backgrounds with varying illumination. Hjelmas E, Low BK (2001) Face detection: A Survey. Proceedings of the IEEE International Conference on Computational Intelligence and Security (CIS), 106-109. ![]() Guo ZH, Zhou W, Xiao L, Hu X, Zehao Z and Zhou H (2018) Occlusion face detection technology based on facial physiology. Proceedings of the International Conference on Biometrics (ICB), 229-236 IEEE Trans Inf Forensics Secur 8(1):239–253Ĭheney J, Klein B, Jain AK, Klare BF (2015) Unconstrained face detection: state of the art baseline and challenges. Multimed Tools Appl 75:365–380īonnen K, Klare BF, Jain AK (2013) Component-based representation in automated face recognition. This technique can be useful in the surveillance and security related applications.īellil W, Brahim H, Amar CB (2016) Gappy wavelet neural network for 3D occluded faces: detection and recognition. The proposed technique improved results in terms of Accuracy, Detection Rate, False Detection Rate and Precision. The authors have presented a face detection technique using a combination of YCbCr, HSV and L × a × b color model. This article proposes an efficient technique for face detection from still images under occlusion and non-uniform illumination. This involves making the machine intelligent enough to acquire the human perception and knowledge to detect, localize and recognize the face in an arbitrary image with the same ease as humans do it. The images in this manual dataset have been taken from the internet. One manual dataset has also been created for experimental purpose. Experimental images have been taken from public dataset AR face dataset and Color FERET dataset. ![]() Experimental work has been conducted on images having problem of face occlusion and non-uniform illumination. The main objective of this article is to detect face in still image. So, these reasons motivate us to do research in field of face detection, especially with problems of face occlusion and non-uniform illumination. It directly affects the efficiency of applications linked with face detection, example face recognition, surveillance, etc. But in field of face detection, especially with problems of face occlusion and non-uniform illumination, not so much work has been done. A lot of work has been done in field of face recognition. Also, many challenges associated with face detection, increases the value of TN (True Negative). Face detection is taken for granted primarily focus is on face recognition. In face recognition, face detection is taken not so seriously. Face detection is important part of face recognition system.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |