Biometrics is becoming an important technology in automated person recognition. With the help of biometrics, the individuals are recognized through their unique characteristics and behaviors of various body parts. Some most famous biometrics techniques include the recognition of face, finger prints, iris, gate and signature. This chapter encompasses various biometrics methods used by researchers till date. The chapter depicts the biometrics under various categories such as biological and behavioral biometrics. This will help the readers to consider various biometrics while designing human recognition systems. Apart from the benefits, biometrics is also susceptible to hacking. The authors’ findings with benefits and drawbacks of biometrics are also discussed in this chapter.

An Overview of Biometrics Methods

There is a noticeable tendency to apply deep convolutional neural network (CNN) in facial identification, since it is able to boost performance in face recognition and verification. However, due to the users have unique facial, exposure of face template to adversaries can severely compromise system security and users' privacy. Here, the authors propose a face template protection technique by using multi-label learning, which maps the facials into low-density parity-check (LDPC) codes. Firstly, a random binary sequence is generated to represent a user and further hashed to produce the protected template. During the training, the random binary sequences are encoded by an LDPC encoder to produce diverse binary codes. Based on carefully designed deep multi-label learning, the facial features of each user are mapped to a diverse binary code. In the process of recognition and verification, the deep CNN mapping architecture is modelled as a Gaussian channel, while the noise brought by intra-variations in the outputs of CNN can be removed by the LDPC decoder. Thus, a robust face template protection scheme is achieved. The simulation results on PIE and extended Yale B indicate that the proposed scheme achieves high genuine accept rate at 1% false accept rate.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-bmt.2018.5156

Face template protection using deep LDPC codes learning

Biometric systems gather information from the person's biometric attributes, used extensively to authorize the individuals. Due to the obvious convenience of using specific individual traits such as face, fingerprints, palm veins, and irises, biometric authentication is becoming more common. In particular, Iris systems are in high demand for high-assurance applications, because they contain a broad feature set and remain stable. Authentication methods based on iris biometrics are now commonly used in a variety of fields. This is due to the fact that iris biometric authentication is both safer and more comfortable than conventional passwords. Template Security is a major concern in biometric systems. The template security mechanism ensures reusable, permanent, and un-linkable models. The Fuzzy Vault strategy is one of the most popular security schemes for Template protection.
Fuzzy vault has demonstrated to be an effective protection method but lacks revocability and security attacks. This article introduced an improved fuzzy vault system. The improved fuzzy vault system was introduced, which uses more than one key to protect biometric data. Different keys make the search space more detailed. The additional key was used to encrypt vault data, which stopped the intruder from accessing the information on the person's biometry. The system was tested using CASIA.v1 and IITD.v1 datasets, and findings showed that the system ensures the protection and authentication of the iris templates without compromising performance. The proposed modification gave a 0.0 % False Accepted Rate (FAR) for both the dataset and False Rejected Rate (FRR), 0.14 % for CASIA v1 and 0.12 % for ITTD v1 False Rejected rate

/pdf/a-fuzzy-vault-development-based-on-iris-images-c72xs95rqy.pdf

A fuzzy vault development based on iris images

The fuzzy commitment scheme (FCS) is one of the most effective biometric cryptosystems (BCs) that provide secure management of cryptographic keys using biometric templates. In this scheme, error correcting codes (ECCs) are firstly employed to encode a cryptographic key into a codeword which is then secured via linking (committing) it with a biometric template of the same length. Unfortunately, the key length is constrained by the size of the adopted biometric template as well as the employed ECC(s). In this paper, we propose a secure iris template protection scheme that combines cancelable biometrics with the FCS in order to secure long cryptographic keys without sacrificing the recognition accuracy. First, we utilize cancelable biometrics to derive revocable templates of large sizes from the most reliable bits in iris codes. Then, the FCS is applied to the obtained cancelable iris templates to secure cryptographic keys of the desired length. The revocability of cryptographic keys as well as true iris templates is guaranteed due to the hybridization of both techniques. Experimental results show that the proposed hybrid system can achieve high recognition accuracy regardless of the key size.

/pdf/effective-combination-of-iris-based-cancelable-biometrics-cmnhpbz0x1.pdf

Effective Combination of Iris-based Cancelable Biometrics and Biometric Cryptosystems

Biometric recognition systems provide security to systems by preventing unauthorized access. In recent times, research proved that unprotected biometric systems can be invaded by hackers. A spoofed biometric security can be a great risk as a compromised biometric template can be lost forever. It can also be used to gain unauthorized access into systems. There is the need to protect biometric template from hackers and possible misuse. This study presents various template protection schemes in literature. In addition, this paper reviewed fuzzy vault biocryptosystem template protection scheme by comparing some fuzzy vault schemes in literature based on their False acceptance rates (FAR) and Genuine acceptance rates (GAR) with different degrees of polynomial. The study also reported past studies with the lowest and the highest FAR as 0.00% and 0.87% respectively while the lowest and the highest GAR stand at 72% and 99% respectively.

Biocryptosystems for Template Protection: A Survey of Fuzzy Vault

Biometric Systems are the personal identification systems that use behavioral and physiological characteristics of a person. One of the main concerns in biometrics systems is template security. Fuzzy vault, a bio-cryptosystem, is used to provide security to the stored templates. Fuzzy vault has proven to be a very good security technique, nonetheless it lacks in providing revocability and security against correlation attacks. Thus for the enhancement of the security of fuzzy vault and to overcome the limitation of correlation attack, techniques like hybrid model and multimodal biometrics can be used. This paper gives a review of the above mentioned techniques, viz. hybrid and multimodal, and how they can be effective in enhancing the security of the system.

Techniques for Enhancing the Security of Fuzzy Vault: A Review

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Techniques for Enhancing the Security of Fuzzy Vault: A Review