Abstract
Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics, especially in the local feature representation phase. However, the study of acutance in this application has not received a lot of attention. Therefore, in this paper we propose that there is an optimal range of image acutance in hyperspectral hand biometrics. To locate this optimal range, a thresholded pixel-wise acutance value (TPAV) is firstly proposed to assess image acutance. Then, through convolving with Gaussian filters, a hyperspectral hand image was preprocessed to obtain different TPAVs. Afterwards, based on local feature representation, the nearest neighbor method was used for matching. The experiments were conducted on hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) databases containing 53 bands. The results that achieved the best performance were those where image acutance was adjusted to the optimal range. On average, the samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the HDHV and HPV datasets, respectively. Furthermore, our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral. From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics.
Highlights
Hand biometrics has been largely studied in the last few decades [1,2,3,4,5] because of its effectiveness in personal authentication
The samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) datasets, respectively
Hyperspectral technology known from remote sensing [15,16,17,18], has been introduced in biometrics, where it is applied in high-security scenarios [18]
Summary
Hand biometrics has been largely studied in the last few decades [1,2,3,4,5] because of its effectiveness in personal authentication. To explore more elaborate features, Zhang et al [34] proposed a local CompCode (competitive code) method for online palmprint recognition, which was based on Gabor features Later, this was developed into a discriminative and robust CompCode by Xu et al [35]. Zhang et al [42] improved the recognition performance by converting high-quality, single-spectral images to low acutance images. Hyperspectral biometrics possess the properties of uniqueness, liveness detection, and anti-spoofing that are difficult to achieve by single-band spectral images. Inspired by [42], in this paper we explore the performance of hyperspectral hand biometrics by filtering these images to have different acutance. The hypothesis we propose is that there exists an optimal range in image acutance for a set of hyperspectral hand images, and when this set is filtered in this range, the recognition performance will be improved.
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