title: Gaussian multiscale aggregation applied to segmentation in hand biometrics.
abstract: 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.
authors.affiliation: Group of Biometrics, Biosignals and Security, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, 28223 Pozuelo de Alarcón, Madrid, Spain. alberto@cedint.upm.es
authors.forename: Alberto
authors.initials: A
authors.lastname: de Santos Sierra
authors.forename: Carmen Sánchez
authors.initials: CS
authors.lastname: Avila
authors.forename: Javier Guerra
authors.initials: JG
authors.lastname: Casanova
authors.forename: Gonzalo Bailador
authors.initials: GB
authors.lastname: del Pozo
mesh_terms: D000465:Algorithms; D001699:Biometry; D006225:Hand; D006801:Humans