Iris Image Recognition Based on Independent Component Analysis and Support Vector Machine

Iris has a very unique texture and pattern, different for each individual and the pattern will remain stable, making it possible as biometric technology called iris recognition. In this paper, 150 iris image from Dept. Computer Science, Palacky University in Olomouc iris database used for iris recognition based on independent component analysis and support vector machine. There are three steps for developing this research namely, image preprocessing, feature extraction and recognition. First step is image preprocessing in order to get the iris region from eye image. Second is feature extraction by using independent component analysis in order to get the feature from iris image. Support vector machine (SVM) is used for iris classification and recognition. In the end of this experimental, the implement method will evaluated based upon Genuine Acceptance Rate (GAR). Experimental result shown that the recognize rate from variation of training data is 52% with one data train, 73% with two data train and 90% three data train. From experimental result also shows that this technique produces good performance.

PENGENALAN INDIVIDU MELALUI IRIS MATA MENGGUNAKAN KOMBINASI METODE INDEPENDENT COMPONENT ANALISIS (ICA) DAN SUPPORT VECTOR MACHINE (SVM) “INDIVIDUAL RECOGNITION USING IRIS ANALYSIS WITH COMBINATION METHOD OF INDEPENDENT COMPONENT ANALYSIS (ICA) AND SUPPOR

ABSTRAKSI: Iris mata adalah salah satu bagian tubuh yang unik dan stabil. Karena itu sangat memungkinkan digunakan sebagai alat identifikasi pengenalan individu. Dalam tugas akhir ini dibuat suatu sistem pengenalan individu melalui citra digital iris mata dengan menggabungkan Independent Component Analysis (ICA) sebagai algoritma ekstraksinya dan Support Vector Machine (SVM) sebagai metode pengklasifikasiannya.Alasan penggunaan ICA adalah untuk menghasilkan fungsi dasar yang maksimal yang dapat merepresentasikan ciri khas iris mata secara efisien. Sedangkan SVM adalah metode learning machine yang bekerja atas prinsip Structural Risk Minimization (SRM) dengan tujuan menemukan hyperplane terbaik yang memisahkan dua buah class pada input space.Dalam prakteknya, citra digital dari iris mata diambil, kemudian melalui proses akuisisi citra dan tahap preprosessing. Citra keluaran dari tahap preprosessing akan diubah kebentuk sinyal iris dengan fungsi gaussian. Setelah itu, sinyal iris diekstraksi menggunakan algoritma pengekstraksian ICA untuk kemudian disimpan kedalam database. Pengklasifikasian database akan dilakukan dengan metode Multiclass SVM.Hasil pengujian sistem telah dianalisis dan dievaluasi sehingga didapatkan sistem dengan akurasi lebih tinggi, waktu deteksi lebih cepat, dan mampu menangani database yang lebih banyak dibandingkan sistem yang pernah dibuat sebelumnya.Kata Kunci : Pengenalan Iris mata, Biometrik, Support Vector Machine (SVM),ABSTRACT: Iris is one of the unique and stable parts of human body. That’s why iris is possibly used for individual identification. In this final project, an individual identification system is made through iris digital image by combining the Independent Component Analysis (ICA) as extraction algorithm and Support Vector Machine (SVM) as the classification method.The reason of using ICA is to produce a maximum basic function which can represent feature of iris efficiently. On the other hand SVM is a learning machine method that works based on Structural Risk Minimization (SRM) in order to find the best hyperplane which is dividing input space into two classes.In practice, digital image from iris is taken then processed through image acquisition process and preprocessing stage. Output image from preprocessing stage will be changed into iris signal form by using Gaussian function. After that, iris signal is extracted by using ICA extraction algorithm to be saved in database. The database classification will be done by using Multiclass SVM.The system testing result has been analyzed and evaluated so that the system with higher accuracy, faster detection time, and system that can handle more database than the previous system can be found.Keyword: Iris Identification, Biometrics, Support Vector Machine (SVM)