• • Fusion: Fingernail plates have been seen to provide appreciable biometric authentication results, when these are fused with finger knuckles [5]. Also, the performance of the fusion of multiple fingernail plates may be a prospective scope, something which has been explored to a certain extent in this work. The fingernail plate may also be investigated in combination with other biometric traits which do not belong to the dorsal part of the human hand like fingerprint, iris, face, and voice. However, such systems would require sample acquisition in multiple steps.
  • • Application in Forensics: Drug analyses in nails have received significant attention because of the ability of nails to amass drugs, when subjected to long-term exposure [38]. Nail plates may be subjected to exhaustive experimentation in order to observe the changes or deformities caused by long- or short-term drug abuse. Additionally, trace evidences obtained from crime scenes encapsulating fingernail plates (as shown in Figure 9.2) shall be able to provide considerable help in forensic investigation.

Acceptability: The sample collection of fingernail plate images can be done using a very low-cost camera, without any constraints being imposed on the user. This has been implemented in the current work. As such, sample collection of this trait may be considered to be considerably user-friendly as it does not require active user cooperation.

• Spoofing: The nail plate enjoys a certain degree of advantage over popular hand biometrics like fingerprints. This is because people inadvertently leave behind fingerprints on whatever they touch, which is not possible in the case of nail plates. However, it would be significant to work towards designing anti-spoofing techniques particularly aimed towards nail plates.


This work has tried to further explore the fingernail plate as a biometric trait, considering three different deep learning methods for feature extraction. The nail plate has anatomically distinctive features which are less prone to impersonation.

This report analyses the performance of the nail plate in unimodal verification and identification systems. The results show that the nail plates under all the three considered feature extraction techniques provide substantially significant authentication performance.

Multimodal systems are known to compensate the drawbacks of a trait and to provide improved performance accuracy. In view of this, the fingernail plate has been explored in various multimodal systems. Score-level fusion has been implemented through four different rules for all three deep learning models. The results show that the multimodal verification systems perform much better than their unimodal counterparts. Various multimodal identification systems have been designed using different weighted and non-weighted fusion rules. The weights of the three nail plates for the weighted fusion methods have been attributed through empirical computation, and by using PSO in order to ensure optimal weight attribution. Comprehensive experiments have been carried out, and results depict that identification accuracy as high as 99.44% can be achieved when right weightage is given to the traits. The results also confirm the fact that more the depth of the model, better the results.

The significant results given by the experiments performed in this work give inspiration to further probe the scope of the nail plate in biometric authentication. It shall be interesting to design the implemented system in an adaptive framework [29], which shall lessen the computational time and cost. This is because such a system shall provide the results after deciding on the optimal fusion rule for the selected biometric traits. The future scope of this proposed work includes checking its efficacy in feature-level multibiometric systems. A larger database also may be prepared so as to scale up research in this field.


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