Abstract
Face recognition is a difficult task mostly because of the inherent variability of the image formation process ranging from the position/cropping of the face and its environment (distance and illumination) is totally controlled, to those involving little or no control over the background and viewpoint. Moreover, those are allowing for major changes in facial appearance due to factors expression, aging, and accessories such as glasses or changes in hairstyle. A solution has been proposed by considering hybrid classification architectures deal with the benefit of robustness via consensus provided by ensembles of Radial Basis Functions (RBF) networks and categorical classification using decision trees. A specific approach considers an ensemble of RBF Networks through its ability to cope with variability in the image formation. The experiments were carried out on images drawn randomly 50 unique subjects totalling to 500 facial images with rotation ± 50 encoded in greyscale. The faces are then normalized to account for geometrical and illumination changes using information about the eye location. Specifically performance true positive by Ensambles RBF1 (ERBF1) increased on ± 13,86% measures up to RBF while ERBF2 by ± 15,93%. On the contrary the false negative rate decreased by amount of ±5,8% for ERBF1 and somewhat less to ±5,6% for ERBF2. When the connectionist ERBF model is coupled with an Inductive Decision Tree - C4.5 - the performance improves over the case while only the connectionist ERBF module is used. Keywords: face recognition, radial basis function networks, hybrid classification, inductive decision tree.
Pendahuluan
Dewasa ini, teknologi informasi yang kian berkembang telah banyak menghasilkan berbagai aplikasi menggunakan citra wajah sebagai sumber informasi. Hal ini dikarenakan secara umum citra wajah dapat memberikan informasi khusus berkaitan dengan identifikasi personal namun sebagai input, citra wajah mempunyai variasi yang tinggi. Secara umum variasi tersebut dapat disebabkan oleh dua hal yaitu variasi pada wajah itu sendiri dan variasi karena transformasi obyek wajah menjadi citra wajah. Penyebab pertama dapat berupa variasi pada detil - detil kecil dari wajah, misalkan ekspresi, pose atau adanya perubahan asesoris wajah. Sedangkan variasi yang disebabkan oleh proses pengambilan citra dapat berupa variasi sudut pengambilan gambar, latar belakang ataupun intensitas. Variasi dari citra wajah tersebut harus mampu diselesaikan oleh sistem pengenalan.
Peneliti: Rully Soelaiman
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