Pengenalan Wajah Menggunakan Metode Elastic Bunch Graph Matching

Pada saat ini, sistem pengenalan wajah sudah banyak digunakan di berbagai aplikasi dan juga metode yang digunakan. Namun terdapat beberapa permasalahan eksternal yang biasa terjadi dalam proses pengenalan wajah yaitu Pose, Illumination, and Expression(PIE). Permasalahan tersebut menyebabkan citra wajah orang yang sama akan dikenali berbeda oleh sistem. Metode Elastic Bunch Graph Matching dapat mengatasi permasalahan tersebut karena proses pengenalan wajah menggunakan titik yang diambil secara manual. Oleh sebab itu pada Tugas Akhir ini membahas pengenalan wajah menggunakan metode Elastic Bunch Graph Matching. Pada metode ini wajah direpresentasikan sebagai graph yang dibentuk dari titik titik fitur yang dibuat secara manual. Setelah mengetahui titik-titik fitur pada wajah, dilakukan perhitungan untuk mendapatkan nilai Jet yang dilanjutkan dengan pembentukan Face Bunch Graph untuk proses pencocokan pada Elastic Bunch Graph Matching. Hasil penelitian menunjukkan bahwa metode ini dapat diterapkan pada pengenalan wajah dengan akurasi 91.67%. Dan dapat mengatasi permalsahan Pose, Illumination, and Expression (PIE) dengan akurasi 70%. Kata Kunci: Pengenalan Wajah, Elastic Bunch Graph Matching, Gabor Wavele

Face recognition with variation in pose angle using face graphs

Automatic recognition of human faces is an important and growing field. Several real-world applications have started to rely on the accuracy of computer-based face recognition systems for their own performance in terms of efficiency, safety and reliability. Many algorithms have already been established in terms of frontal face recognition, where the person to be recognized is looking directly at the camera. More recently, methods for non-frontal face recognition have been proposed. These include work related to 3D rigid face models, component-based 3D morphable models, eigenfaces and elastic bunched graph matching (EBGM). This thesis extends recognition algorithm based on EBGM to establish better face recognition across pose variation. Facial features are localized using active shape models and face recognition is based on elastic bunch graph matching. Recognition is performed by comparing feature descriptors based on Gabor wavelets for various orientations and scales, called jets. Two novel recognition schemes, feature weighting and jet-mapping, are proposed for improved performance of the base scheme, and a combination of the two schemes is considered as a further enhancement. The improvements in performance have been evaluated by studying recognition rates on an existing database and comparing the results with the base recognition scheme over which the schemes have been developed. Improvement of up to 20% has been observed for face pose variation as large as 45°