Facial Recognition on System Prototype to Verify Users using Eigenface, Viola-Jones and Haar

Robin Robin; Aldrick Handinata; Wenripin Chandra

doi:10.47709/cnahpc.v3i2.1058

Authors

Robin Robin Universitas Pelita Harapan Medan
Aldrick Handinata Universitas Pelita Harapan Medan
Wenripin Chandra STMIK Mikroskil

DOI:

10.47709/cnahpc.v3i2.1058

Keywords:

Eigenface, Facial Recognition, OpenCV, System Prototype, Viola-Jones, Haar Feature

Dimension Badge Record

Abstract

Facial recognition is one of the most popular way to authenticate user into a system. This method is preferable considering the tendency of users for using the same password across multiple sites which made the user has already made his own account securities in vulnerable states. Using biometrics might supply solutions to solve this problem and facial recognition is one of the best biometric methods can be apply as a digital account security solution. This study to design a prototype system implementing facial recognition to verify users to measure how accurate these methods are. The method used here is Viola-Jones for face detection, Eigenface and Haar feature for face recognition from the OpenCV. The system was designed in Java. Based on the test results from the system designed, system can recognize user face with 100% accuracy if faces are shot in a well desirable condition. The system is able to recognize the user's face with various expressions including with or without glasses. However, the system has difficulty in recognizing user’s face in facing up, down, sideways position or blocked by accessories or body parts such as hands. After some experiment, it was proven that the system designed is accurate, reliable and safe enough to be implemented to digital authorization process.

Downloads

Download data is not yet available.

Google Scholar Cite Analysis
Abstract viewed = 359 times

References

Afiyat, N. (2017). Analisis Restorasi Citra Kabur Algoritma Wiener Menggunakan Indeks Kualitas Citra. Nusantara Journal of Computers and Its Applications, 24-34.

Alexander, L. W., Sentinuwo, S. R., & Sambul, A. M. (2017). Implementasi Algoritma Pengenalan Wajah Untuk Mendeteksi Visual Hacking. E-Journal Teknik Informatika.

Chau, S., Banjarnahor, J., Irfansyah, D., Kumala, S., & Banjarnahor, J. (2019). Analisis Pendeteksian Pola Wajah Menggunakan Metode Haar-Like Feature. JITE (Journal of Informatics and Telecommunication), 69-76.

Chowhan, R. S., & Tanwar, R. (2019). Password-Less Authentication: Methods for User Verification and Identification to Login Securely Over Remote Sites. Machine Learning and Cognitive Science Applications in Cyber Security, 190-212.

Damanik, R. R., Sitanggang, D., Pasaribu, H., Siagian, H., & Gulo, F. (2018). An Application of Viola-Jones Method For Face Recognition For Absence Process Efficiency. Journal of Physics, 1-8.

Detila, Q. M., & Wibowo, E. P. (2019, Desember). Perbandingan Metode Eigenface, Fischerface, dan LBPH pada Sistem Pengenalan Wajah. Jurnal Ilmiah KOMPUTASI, 18, 315-322.

Ether, W. (2017). Introduction to Artificial Intelligence. Ravensburg: Springer.

Krasmala, R., Purba, A. B., & Lenggana, U. T. (2017). Kompresi Citra Dengan Menggabungkan Metode Discrete Cosine Transfor (DCT) dan Algortima Huffman. JOIN, 1-9.

Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2020). Deep Learning for Generic Object Detection: A Survey. International Journal of Computer Vision, 261-318.

Mahmudi, Z, M. F., & Kusrini. (2019). Implementasi Metode Viola Jones Untuk Mendeteksi Wajah Manusia. Jurnal INFORMA Politeknik Indonusa Surakarta, 54-60.

Manurung, E. F. (2017). Implementasi Metode Median Filter dan Histogram Equalization Untuk Perbaikan Citra Digital. Jurnal Pelita Informatika, 67-71.

Raharja, B. D., & Harsadi, P. (2018). Implementasi Kompresi Citra Digital dengan Mengatur Kualitas Citra Digital. Jurnal Ilmiah Sinus, 71-77.

Ramadhan, B. W., Jati, A. N., & Ahmad, U. A. (2016). Implementasi Eigenface Untuk Pengenalan Wajah Pada Building Security System Berbasis Embedded. e-Proceeding of Engineering, 764-770.

Rosyani, P. (2017). Pengenalan Wajah Menggunakan Metode Principal Component Analysis (PCA) dan Canberra Distance. Jurnal Informatika Universitas Pamulang, 118-121.

S, A. B., Suma'inna, & Maulana, H. (2016). Pengenalan Citra Wajah Sebagai Identifier Menggunakan Metode Principal Component Analysis (PCA). Jurnal Teknik Informatika, 166-175.

Sinaga, A. S. (2017). Implementasi Teknik Thresholding Pada Segmentasi Citra Digital. Jurnal Mantik Penusa, 48-51.

Sulistyawati, D. H., & Utomo, H. S. (2016). Perbaikan Citra Dengan Noise Mission Block Menggunakan Implementasi Algoritma Projection Onto Convex Sets (POCS). KONVERGENSI, 11-19.

Utari, C. T. (2016). Implementasi Algoritma Run Length Encoding Untuk Perancangan Aplikasi Kompresi dan Dekompresi File Citra. Jurnal TIMES, 24-31.

Yang, H., & Wang, X. A. (2016). Cascade Classifier for Face Detection. Journal of Algorithms & Computational Technology, 187-197.