Neural Network Based Footprint Identification Without Feature Extraction

被引：0

作者：

Kurban, Onur Can ^{[1
]}

Yildirim, Tulay ^{[1
]}

Basaran, Emrah

机构：

[1] Yildiz Tekn Univ, Elekt & Haberlesme Muhendisligi Bolumu, Istanbul, Turkey

来源：

2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2013年

关键词：

Biometrics; footprint; identification; PCA; classification;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In recent years, identification systems with using biometric features are receiving considerable attention. Iris, palmprint, fingerprint and footprint are shown as examples. This paper focused on footprint identification without features extraction. CASIA Database, Dataset-D used for identification database. Dataset-D contain footprint images taken from foot pressure measurement plate. Firtsly, each RGB image converted gray scale and resized the fifth and resized 30x15 matrix. In the end, each 30x15 matrix is converted to 1x450 input array, and simulated by MLP, SVM and Naive-Bayes classifiers. The best result without features extraction achived by MLP classifier.

引用

页数：4