Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics

Sensors

Alberto de Santos Sierra¹  Carmen Sánchez Ávila²  Javier Guerra Casanova² 

[1] Group of Biometrics, Biosignals and Security, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, 28223 Pozuelo de Alarcón, Madrid, Spain;

关键词: hand biometrics;    multiscale aggregation;    image segmentation;    image processing;    biometrics;    security;   

DOI  :  10.3390/s111211141

来源: mdpi

【 摘 要 】

This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.