Yopi, _ (2013) RANCANG BANGUN MODEL WAJAH UNTUK MENDUKUNG PROSES IDENTIFIKASI WAJAH DENGAN METODE MODIFIED HAUSDORFF DISTANCE (MHD) DAN PRINCIPAL COMPONTENT ANALYSIS (PCA) PADA IMAGE SEQUENCE. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Wajah merupakan salah satu biometrik pada manusia yang memiliki kelebihan jika dibandingkan dengan biometrik lain. Beberapa kelebihan wajah diantaranya mudah dikenali baik oleh mesin ataupun oleh manusia, dapat diambil walaupun dari jarak yang jauh, dan merupakan passive technology (dapat bekerja walaupun tanpa kerjasama user). Hal tersebut menjadikan proses identifikasi wajah digunakan pada teknologi surveillance (pengawasan) di tempat umum seperti di bandara. Identifikasi wajah merupakan gabungan antara proses deteksi wajah dan proses pengenalan wajah. Deteksi wajah berperan pada proses face localization, yaitu proses untuk mencari ukuran dan posisi wajah pada citra. Sedangkan pengenalan wajah berperan pada proses pencocokan wajah yang terdeteksi dengan data wajah yang ada pada database, sehingga dapat identitas pemilik wajah dapat diketahui. Penelitian ini mencoba untuk mengimplementasikan proses deteksi wajah dan proses pengenalan wajah menggunakan metode modified hausdorff distance (MHD) dan principal component analysis (PCA). Secara sederhana MHD bekerja dengan menghitung nilai kedekatan model wajah (face model) dengan citra yang diproses. Model wajah yang digunakan pada proses deteksi wajah merupakan hasil training 100 training image menggunakan algoritma genetika. Pada proses pengenalan wajah, PCA bekerja dengan menghitung nilai eigenvector matrik piksel citra, kemudian dihitung nilai weight-nya. Selanjutnya nilai weight tersebut dibandingkan dengan nilai weight yang ada pada basis data, dimana nilai weight berkorelasi dengan data identitas. Akurasi proses deteksi wajah mencapai 62.73% dan akurasi proses pengenalan wajah mencapai 96%.;--- Face is one of human biometric which have advantages compared other human biometric. Some advantages faces both of which are easily recognizable by machine or by human, can be taken from long distance, and it is passive technology (can work even without user cooperation). This makes the identification proccess used on surveillance technology in public places such as at airport. Facial identification is a combination of the detection process and recognition process. Face detection role in the localization process, which is a process to find the size and position of the face in the image. While face recognition plays a role in the process of matching faces detected by the face data in the database, so the owner of the face can be identified. This study attempts to implement face detection process and face recognition process using the modified Hausdorff distance (MHD) and principal component analysis (PCA). In a simple MHD work by calculating the value of the proximity model of the face (face model) with image processed. Face model used in face detection process is the results of training 100 training image using a genetic algorithm. In the process face recognition, PCA works by calculating the eigenvector matrix pixel image, and then calculated the value of its weight. Furthermore, the weight value compared with the weight values in the database, where the value of weight correlated with identity data. The accuracy of face detection process reaches 62.73% and the accuracy of the face recognition reached 96%.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | No.panggil : S IKOM YOP r-2013; Pembimbing : I.Herbert Siregar, II.Enjang Ali Nurdin. |
| Uncontrolled Keywords: | Pengenalan Wajah, Deteksi Wajah, Surveillance, Modified Hausdorff Distance, Principal Component Analysis, Face Recognition, Face Detection, Surveillance, Modified Hausdorff Distance, Principal Component Analysis. |
| Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) |
| Divisions: | Fakultas Pendidikan Matematika dan Ilmu Pengetahuan Alam > Program Studi Ilmu Komputer |
| Depositing User: | Mr mhsinf 2017 |
| Date Deposited: | 27 Dec 2017 23:56 |
| Last Modified: | 27 Dec 2017 23:56 |
| URI: | http://repository.upi.edu/id/eprint/28380 |
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