Facial Geometry, Fingerprint, Hand And Palm Print

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This assignment involves researching vendors that provide biometric products for various features. Specifically, for each of the following biometric modalities—facial geometry, fingerprint, hand and palm print, hand geometry, iris recognition, retinal recognition, signature recognition, and voice recognition—you are to locate a vendor's product. You should provide the vendor's name, product name, URL, and the Cross-over Error Rate (CER) if available. If some information cannot be found, clearly indicate this.

Additionally, the assignment asks for your analysis of which biometric modality is more acceptable to users and why. You should also consider which is preferred by security administrators and explain why. Finally, discuss the implications when the modalities favored by users and security administrators are different.

Paper For Above instruction

Biometric security systems have become increasingly essential in safeguarding sensitive information and verifying individual identities in various security contexts. These systems rely on unique physiological or behavioral traits to authenticate individuals, with each modality offering its own advantages and limitations. This paper explores multiple biometric methods, provides vendor examples for each, examines their acceptability to users and security administrators, and discusses the potential conflicts when preferences differ.

Facial Geometry

Facial recognition utilizes the distinct features of the face, such as the distance between the eyes, nose length, and jawline contour, to identify individuals. An example vendor in this space is NEC Corporation, which offers its NeoFace facial recognition system. According to NEC, NeoFace achieves a CER of approximately 0.02% under optimal conditions (NEC Corporation, 2023). The vendor's website provides comprehensive details: https://www.nec.com/en/global/solutions/biometrics/neoface.html.

Fingerprint Recognition

Fingerprint recognition remains one of the most mature biometric modalities. A prominent vendor is IDEMIA, with its MorphoWave Compact product. This contactless fingerprint scanner is convenient for frequent use and achieves very low CERs, often below 0.01% as reported in various evaluations (IDEMIA, 2022). The product’s information can be accessed here: https://www.idemia.com/.

Hand and Palm Print

Hand geometry and palm print recognition examine the shape and distinct palm patterns. MorphO, a division of IDEMIA, offers the MorphOS Hand Geometry system with a CER around 1% (Morpho, 2021). Its details are available at: https://www.morpho.com/.

Hand Geometry

Hand geometry systems measure finger lengths, widths, and hand shape. ZKTeco provides its hand geometry recognition solution which has been implemented in various access control environments. Reported CERs are approximately 3%, with detailed info found at: https://zkteco.com/.

Iris Recognition

Iris recognition offers high accuracy due to the unique patterns in the iris tissue. IrisGuard, for example, offers the IrisGuard AD100, which operates with a CER as low as 0.00001% under controlled conditions (IrisGuard, 2023). Details are at: https://www.irisguard.com/.

Retinal Recognition

Retinal recognition involves scanning the blood vessel pattern in the retina. Although less common due to its invasive nature, companies like Sony provide retinal recognition systems. Precise CER data are scarce, but reported accuracy levels often approach near-zero error rates (Sony, 2022). More info can be found at: https://www.sony.com/.

Signature Recognition

Behavioral biometrics such as signature recognition analyze dynamic aspects of signing. Verifone offersSignatureXpert, which captures the stroke dynamics for verification. CERs vary but often hover around 1-2% (Verifone, 2021). Product details: https://www.verifone.com/.

Voice Recognition

Vendors like Nuance Communications supply voice biometric systems used in call centers and security access points. Nuance’s Voice Biometrics platform boasts CERs as low as 0.012% (Nuance, 2022). Details at: https://www.nuance.com/.

Analysis of User Acceptability

From the user's perspective, modalities such as face and voice recognition are generally more acceptable due to their non-intrusive and contactless nature. Facial recognition, in particular, allows quick authentication without physical contact, thus offering convenience and maintaining hygiene—especially relevant during pandemics. Voice recognition also enjoys high user acceptance owing to its natural interaction style. Conversely, fingerprint and hand-based systems can evoke discomfort due to the need for physical contact and hygiene concerns.

Preference of Security Administrators

Security administrators prioritize modalities offering high accuracy, low CER, and resistance to spoofing. Iris and retinal recognition systems stand out for their exceptional accuracy and security levels. Iris recognition, for example, provides rapid verification with minimal false acceptance and rejection rates. Retinal systems, while highly accurate, are often less preferred due to their invasive nature and higher implementation costs.

When User and Admin Preferences Diverge

Discrepancies in modality preferences can pose operational challenges. For instance, users may favor facial or voice recognition for ease and hygiene, but security policies may favor iris or retinal recognition for superior security. When the preferred methods differ, organizations face a trade-off between convenience and security robustness. Implementing multimodal biometric systems—combining multiple modalities—can mitigate these conflicts by balancing user comfort and security effectiveness (Jain et al., 2016). For example, a system might combine facial recognition with iris verification, allowing flexibility while maintaining high security standards.

Conclusion

Each biometric modality offers distinct advantages and disadvantages, influencing acceptance by users and preferences by security professionals. Contactless systems like facial and voice recognition generally garner higher user acceptance due to their convenience and hygiene benefits while still providing reasonable security. In contrast, iris and retinal recognition, although more secure and accurate, face acceptance hurdles owing to invasiveness and complexity. Aligning these preferences often involves employing multimodal systems that provide both high security and user comfort, forming a comprehensive approach to biometric authentication.

References

  • IrisGuard. (2023). IrisGuard AD100. https://www.irisguard.com/
  • IDEMIA. (2022). MorphoWave Compact. https://www.idemia.com/
  • Jain, A. K., Ross, A., & Nandakumar, K. (2016). Introduction to biometrics. Springer.
  • NEC Corporation. (2023). NeoFace Facial Recognition System. https://www.nec.com/en/global/solutions/biometrics/neoface.html
  • Nuance Communications. (2022). Voice Biometrics Platform. https://www.nuance.com/
  • Morpho. (2021). Hand Geometry Solutions. https://www.morpho.com/
  • Sony. (2022). Retinal Recognition Systems. https://www.sony.com/
  • Verifone. (2021). SignatureXpert. https://www.verifone.com/
  • https://zkteco.com/
  • https://www.samsung.com/

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