Pengenalan Wajah Menggunakan Two Dimensional Linear Discriminant Analysis Berbasis Optimasi Feature Fusion Strategy
Abstract
The era of technology today,, research on biometric image is not common to do. One well researched biometric image is a face recognition (face recognition). Problems on the human face recognition is a diversity of features or shape between one another face to face. Therefore, the need for facial feature extraction and classification using a particular method so that the classification can be recognized correctly.In this study proposed feature extraction method that can overcome the problems of non-linear automatic data contained in the face image, called the Two Dimensional Linear Discriminant Analysis based on Feature Fusion Strategy (TDLDA-FFS). Not stopping on feature extraction, classification methods proposed also faces that can overcome the problems of the adaptive matrix which aims to study the benefit of weight on each - each input with the method Relevanced Generalized Learning Vector quantization (GRLVQ).This research integrates methods TDLDA-FFS and GRLVQ for face recognition. With the combination of both methods are proven to provide optimal results with a level of recognition accuracy ranged between 77.78% to 82.22% with a pilot using a databaseof facial images from the Institute of Business and Information Stikom Surabaya. While the test uses a database derived from YaleB Database achieve accuracy levels ranging from 88.89% to 94.44%.
References
[2] Guiyu Feng, Dewen Hu, Zongtan Zhou, 2008, A Direct Locality Preserving Projections (DLPP) Algorithm for Image Recognition, Springer Science, hal 247-255.
[3] Bashyal. S, Venayagamoorthy. G. K., 2008, Recognition of facial expressions using Gabor Wavelets and Learning Vector Quantization, Elsevier, hal 1056-1064.
[4] Turk, M, A. Pentland, 1991, Face recognition using eigenfaces, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Maui, Hawaii,
June.
[5] Belhumeur, P. N, Heespanha, J. P, Kriegman, D. J, 1997, Eigenfaces vs Ficergaces : Recognition Using Class Specific Linear Projection, IEEE Transcations on
Pattern Analysis and Machine Intelligence, vol 19. no.7, hal 711-720.
[6] J. Ye, R. Janardan and Q. Li, 2005, Two-Dimensional Linear Discriminant Analysis, Advances in Neural Information Processing System, hal 1569-1576.
[7] Damayanti, 2010, Pengenalan Citra Wajah Menggunakan Two Dimensional Liner Discriminant Analysis dan Support Vector Machine, Tesis, Program Pasca
Sarjana Teknik Informatika, Institut Teknologi Sepuluh Nopember, Surabaya.
[8] Sinulingga, S, B. Afandi, dan P.A.R. Devi, 2015, Pengenalan Wajah Menggunakan Two Dimensional Linier Discrimininant Analysis berbasis Feature Fusion
Strateg,Jurnal Cybermatika, vol. 3. no.1. hal 23-28.
[9] Chen, W.S, X. Dai, B. Pan, and T. Huang, A novel discriminant criterion based on feature fusion strategy for face recognition,Neurocomputing, vol. 159, hal 67–
77.
[10] Yang, J., Zhang, D., Yang, Y., Niu, B., 2007, Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm
biometrics, IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, hal 650-664
JATIS oleh http://jurnal.mdp.ac.id/index.php/jatisi disebarluaskan di bawah Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional.