Hand shape identification on multirange images

Travieso, Carlos M.; Ticay-Rivas, Jaime R.; Briceño, Juan Carlos; Pozo-Baños, Marcos del; Alonso, Jesús B.

doi:10.1016/j.ins.2014.02.031

Cited by 18 publications

(4 citation statements)

References 34 publications

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“…Several hardware and software-specific fundamental techniques are deployed for PAD in the biometric contexts. In the hardware-based method, an expensive sensor is employed for image acquisition from which the constraints about liveness such as the thermal facial map, blood pressure, perspiration, hand-vein temperature, and other salient features are computed [20], [24], [31], [51], [17]. Technological advancements of the sensor devices offer to acquire the thermal [20], [24], [51], [17], [4] and hyperspectral [25] images, which direct to devise high-security measures such as liveness detection to discard the fake hand biometric samples.…”

Section: Introductionmentioning

confidence: 99%

“…In the hardware-based method, an expensive sensor is employed for image acquisition from which the constraints about liveness such as the thermal facial map, blood pressure, perspiration, hand-vein temperature, and other salient features are computed [20], [24], [31], [51], [17]. Technological advancements of the sensor devices offer to acquire the thermal [20], [24], [51], [17], [4] and hyperspectral [25] images, which direct to devise high-security measures such as liveness detection to discard the fake hand biometric samples. On the contrary, the software-based method discriminates between the real and counterfeit images using a computable characteristic, such as visual quality assessment [57], [45], [49], [52], [29], [48], [30], [37].…”

Section: Introductionmentioning

confidence: 99%

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Spoofing detection on hand images using quality assessment

Bera

Dey

Bhattacharjee

et al. 2021

Multimed Tools Appl

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Recent research on biometrics focuses on achieving a high success rate of authentication and addressing the concern of various spoofing attacks. Although hand geometry recognition provides adequate security over unauthorized access, it is susceptible to presentation attack. This paper presents an anti-spoofing method toward hand biometrics. A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images. A threshold-based gradient magnitude similarity quality metric is proposed to discriminate between the real and spoofed hand samples. The visual hand images of 255 subjects from the Bogazici University hand database are considered as original samples. Correspondingly, from each genuine sample, we acquire a forged image using a Canon EOS 700D camera. Such fake hand images with natural degradation are considered for electronic screen display based spoofing attack detection. Furthermore, we create another fake hand dataset with artificial degradation by introducing additional Gaussian blur, salt and pepper, and speckle noises to original images. Ten quality metrics are measured from each sample for classification between original and fake hand image. The classification experiments are performed using the k-nearest neighbors, random forest, and support vector machine classifiers, as well as deep convolutional neural networks. The proposed gradient similarity-based quality metric achieves 1.5% average classification er-A. Bera

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spoofing detection on hand images using quality assessment

Bera

Dey

Bhattacharjee

et al. 2021

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…Several hardware and software-specific fundamental techniques are deployed for PAD in the biometric contexts. In the hardware-based method, an expensive sensor is employed for image acquisition from which the constraints about liveness such as the thermal facial map, blood pressure, perspiration, hand-vein temperature, and other salient features are computed Czajka and Bulwan [2013], Faundez-Zanuy et al [2014], Harvey et al [2018], Travieso et al [2014], Chen et al [2016]. Technological advancements of the sensor devices offer to acquire the thermal Czajka and Bulwan [2013], Faundez-Zanuy et al [2014], Travieso et al [2014], Chen et al [2016], Bartuzi and Trokielewicz [2018] and hyperspectral Ferrer et al [2014] images, which direct to devise high-security measures such as liveness detection to discard the fake hand biometric samples.…”

Section: Introductionmentioning

confidence: 99%

“…In the hardware-based method, an expensive sensor is employed for image acquisition from which the constraints about liveness such as the thermal facial map, blood pressure, perspiration, hand-vein temperature, and other salient features are computed Czajka and Bulwan [2013], Faundez-Zanuy et al [2014], Harvey et al [2018], Travieso et al [2014], Chen et al [2016]. Technological advancements of the sensor devices offer to acquire the thermal Czajka and Bulwan [2013], Faundez-Zanuy et al [2014], Travieso et al [2014], Chen et al [2016], Bartuzi and Trokielewicz [2018] and hyperspectral Ferrer et al [2014] images, which direct to devise high-security measures such as liveness detection to discard the fake hand biometric samples. On the contrary, the software-based method discriminates between the real and counterfeit images using a computable characteristic, such as visual quality assessment Zhang et al [2013], Reenu et al [2013], Sun et al [2018], Wang et al [2004], Gao et al [2018], Sellahewa and Jassim [2010], Guan et al [2017], Martini et al [2012].…”

Section: Introductionmentioning

confidence: 99%