Face Detection and Recognition is an important area in the field of substantiation. Maintenance of records of students along with monitoring of class attendance is an area of administration that requires significant amount of time and efforts for management. Automated Attendance Management System performs the daily activities of attendance analysis, for which face recognition is an important aspect. The prevalent techniques and methodologies for detecting and recognizing face like PCA-LDA, etc fail to overcome issues such as scaling, pose, illumination, variations, rotation, and occlusions. The proposed system provides features such as detection of faces, extraction of the features, detection of extracted features, analysis of students' attendance and monthly attendance report generation. The proposed system integrates techniques such as image contrasts, integral images, Ada-Boost, Haar-like features and cascading classifier for feature detection. Faces are recognized using advanced LBP using the database that contains images of students and is used to recognize student using the captured image. Better accuracy is attained in results and the system takes into account the changes that occurs in the face over the period of time.

1. Paul Viola, Michael J. Jones, "Robust Real-Time Face Detection" International Journal of Computer Vision Volume 57 Issue 2, pp. 137 – 154, May 2014.
2. J. Chatrath, P. Gupta, P. Ahuja, A. Goel, "Real Time Human Face Detection and Tracking" International Conference on Signal Processing and Integrated Networks(SPIN) Volume 1,Issue 1,pp. 705-710, 2014.
3. R N Daschoudhary, Rajashree Tripathy, "Real-time Face Detection and Tracking Using Haar Classifier" International Journal of Electronics and Computer Science Engineering Volume 3, Issue 2, pp. 175-184, April 2014.
4. Shrinivasa Naika C. L, Pradip K Das, Shivashankar B Nair, "Asymmetric Region Local Binary Pattern For Person-dependent Facial Expression Recognition", International Conference on Computing, Communication and Applications (ICCCA),Volume 3, pp. 173-179 February, 2012.
5. Faudzi, N. Yahya, "Evaluation of LBP-based face recognition techniques",5th International Conference on Intelligent and Advanced Systems (ICCIA), Volume 1, Issue 2, pp. 1-6, June 2014.
6. Abdur Rahim, Najmul Hossain, Tanzillah Wahid, Shafiul Azam, "Face Recognition Using Local Binary Patterns" International Journal of Computer Science and Technology Graphics and Computer Vision, Volume 13, Issue 4, pp. 817-823, 2013.
7. Sapna Vishwakarma, Prof. Krishan Kant Pathak, "Face Recognition using LBP Coefficient Vectors with SVM Classifier" International Journal of Engineering Trends and Technology (IJETT), Volume 9, Issue 2, pp. 96-100, Mar-2014.
8. Zhang, Ligang, Tjondronegoro, Dian W. , Chandran, Vinod, "Discoveringthe best feature extraction and selection algorithms for spontaneous facial expression recognition" IEEE Conference on Multimedia and Expo, Issue 2, pp. 1027-1032, Feb-2012.

Pca-lda Ada-boost Cascading Classifier Background Regions.