IntroductionThe facial recognition has been a problem worked on around the world for many persons; this problem has emerged in multiple fields and sciences, especially in computer science, others fields that are very interested In this technology are: Mechatronic, Robotic, criminalistics, etc. In this article I work in this interesting topic using EmguCV cross platform.Net wrapper to the Intel OpenCV image processing library and C#.Net, these library’s allow me capture and process image of a capture device in real time. Hide Copy Code // Declararation of all variables, vectors and haarcascadesImage currentFrame;Capture grabber;HaarCascade face;HaarCascade eye;MCvFont font = new MCvFont(FONT.CVFONTHERSHEYTRIPLEX, 0.5d, 0.5d);Image result, TrainedFace = null;Image gray = null;List trainingImages = new List;List labels= new List;List NamePersons = new List;int ContTrain, NumLabels, t;string name, names = null;Then load the haarcascades for face detection, then I do a little “procedure” to load of previous trained faces and labels for each image stored previously.

1. Face Recognition Software

Authentication is the process of verifying one’s identity, and one of its implementation is in taking attendances in university’s lectures. Attendance taking is a very important matter to every academic institution as a way to examine students’ performance. Signature based attendance taking can be manipulated.

Therefore it has problems in verifying the attendance validity. In this final project, a real time eigenface based face recognition is implemented in an application to do attendance taking.

The input face image is captured using a webcam. The application itself is built in C#, utilizing EmguCV library. The application is developed using Visual Studio 2015.

Face detection is done with Viola-Jones algorithm. The eigenface method is used to do facial recognition on the detected face image.

In this final project, a total of 8 testings are done in different conditions. From the testings, it is found that this application can recognize face images with accuracy as high as 90% and as low as 6.67%. This solution can be used as an alternative for real-time attendance taking in an environment with 170 lux light intensity, webcam resolution of 320 x 240 pixel, and the subject standing 1 meter away while not wearing spectacles. The average recognition time is 0.18125 ms.

K., Yousaf, M. H., Ahmad, W., & Baig, M. Algorithm for Efficient Attendance Management: Face Recognition based Approach. IJCSI International Journal of Computer Science Issues, 146-150. Pembuatan Aplikasi Presensi Perkuliahan Berbasis Fingerprint (Studi Kasus: Jurusan Sistem Informasi Institut Teknologi Sepuluh Nopember Surabaya).

Jurnal Teknik POMITS, A465-A469. B., & Kurniawan, B. Perancangan Sistem Absensi Kehadiran Perkuliahan dengan Menggunakan Radio Frequency Identification (RFId). Jurnal CoreIT, 44-49. Implementasi Pengenalan Wajah dengan Metode Eigenface pada Sistem Absensi.

Jurnal Coding, Sistem Komputer Untan, 41-50. (P., & Jones, M. Starcraft 2 co op campaign. Rapid Object Detection Using a Boosted Cascade of Simple Features. Computer Vision and Pattern Recognition, 511-518. Sistem Presensi Karyawan Berbasis Pengenalan Wajah dengan Algoritma Eigenface. Yogyakarta: STMIK AMIKOM.Slavkovic, Marijeta & Jevtic, Dubravka. Face Recognition Using Eigenface Approach.

Serbian Journal of Electrical Engineering, 9(No. 1), 121-130.Setiadi, D.

Aplikasi Identifikasi Personal dengan Metode Principle Component Analysis Berbasis Android. Pekanbaru: Politeknik Caltex Riau.Rahman, M. Sistem Pengenalan Wajah Menggunakan Webcam untuk Absensi dengan Metode Template Matching. Surabaya: Politeknik Negeri Surabaya. Sistem Pengenalan Wajah dengan Metode Eigenface untuk Absensi pada PT.

Florindo Lestari. Jakarta: Universitas Budi Luhur. (M., & Pentland, A. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, 3, 71-86.

(P., & Jones, M. (2004) Robust Real-Time Face Detection. International Journal of Computer Vision, 137-154. An Introduction to ROC Analysis.

Face Recognition Software

Pattern Recognition Letters, 861-874. (Sheifali, Sahoo, O. P., Goel, A., & Gupta, R. (2010) A New Optimized Approach to Face Recognition Using Eigenfaces. Global Journal of Computer Science and Technology, 10, 15-17.