الگوریتم تشخیص چهره بهبود یافته
Abstract
۱٫ Introduction
۲٫ Related work
۳٫ Improved LBP algorithm
۴٫ Experiments
۵٫ Attendance system
۶٫ Conclusion
Acknowledgements
Appendix A. Supplementary data
References
Abstract
Face Recognition is a computer application that is capable of detecting, tracking, identifying or verifying human faces from an image or video captured using a digital camera. Although lot of progress has been made in domain of face detection and recognition for security, identification and attendance purpose, but still there are issues hindering the progress to reach or surpass human level accuracy. These issues are variations in human facial appearance such as; varying lighting condition, noise in face images, scale, pose etc. This research paper presents a new method using Local Binary Pattern (LBP) algorithm combined with advanced image processing techniques such as Contrast Adjustment, Bilateral Filter, Histogram Equalization and Image Blending to address some of the issues hampering face recognition accuracy so as to improve the LBP codes, thus improve the accuracy of the overall face recognition system. Our experiment results show that our method is very accurate, reliable and robust for face recognition system that can be practically implemented in real-life environment as an automatic attendance management system.
Introduction
The human face is a sophisticated multidimensional structure that can convey a lot of information about the individual, including expression, feeling, facial features. Effectively and efficiently analyzing the features related to facial information is a challenging task that requires lot of time and efforts. Recently, many facial recognition-based algorithms for automatic attendance management has been proposed, successfully implemented and used as in Refs. [1–4] and also new algorithms developed or some existing algorithms improved or combined with other methods, techniques, or algorithms to build facial recognition systems or applications as in Refs. [5–8]. Although lot of achievements have been made in devising facial recognition algorithms and systems, but to reach human level accuracy of facial recognition, some major issues associated with these algorithms/ systems should be greatly mitigated or addressed as argued in Ref. [9] so as to realize a reliable and accurate facial recognition-based automatic attendance management system, which can be very useful in the area of substantiation. The main challenges for successful face detection and recognition systems are; illumination conditions, scale, occlusion, pose, background, expression etc., as highlighted in Refs. [10,11]. Various algorithms and methods have been proposed to address these challenges; N.Pattabhi Ramaiah Ref. [12] uses illumination Invariant Face Recognition using Convolutional Neural Networks to address illumination conditions, Abass et al Ref. [13] addresses the issues of shift and rotation using complex wavelet transform (CWT) and Fisherface. To address issues related to pose, Kishor et al Ref. [14] proposes robust pose invariant face recognition using Dual Cross Pattern (DCP), LBP and Support Vector Machine (SVM).