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.
Highlights
Sensors 2011, 11 and identification in devices like PC or mobile phones, since hand biometrics system requirements are met with a standard camera and hardware processor
This section contains the results of the comparative evaluation of the proposed approach to Lossy Data Compression (LDC) [7]
The application of hand biometrics to unconstrained and contact-less, platform-free environments implies an increase in difficulty in the pre-processing and segmentation procedure in hand acquisition
Summary
Hand biometrics is receiving an increasing attention at present because of their huge applicability in daily scenarios and the relation between user acceptance and identification/verification rates [1,2]. The proposed approach is based on multiscale aggregation, gathering pixels along scales according to a given similarity Gaussian function This method produces an iterative clustering aggregation, providing a solution for hand image segmentation with a quasi-linear computational cost and an adequate accuracy for biometric applications. The method has been tested with a synthetic image database, with around 408,000 images considering different backgrounds (e.g., soil, skins/fur, carpets, walls or grass) and illumination environments, and compared to two competitive approaches in literature in terms of image segmentation. These approaches are named Lossy Data Compression (LDC) [7] and Normalized Cuts (NCut) [8].
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