• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2420-2433
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• 2014

Fusing the scores of multibiometrics is a very promising approach to improve the overall system's accuracy and the verification performance. In recent years, there are several approaches towards studying score level fusion of several biometric systems. However, most of them does not consider the genuine and imposter score distributions and result in a higher equal error rate usually. In this paper, a novel score level fusion approach of different biometric systems (dual iris, thermal and visible face traits) based on $Acz{\acute{e}}l$-Alsina triangular norm is proposed. It achieves higher identification performance as well as acquires a closer genuine distance and larger imposter distance. The experimental tests are conducted on a virtual multibiometrics database, which merges the challenging CASIA-Iris-Thousand database with noisy samples and the NVIE face database with visible and thermal face images. The rigorous results suggest that significant performance improvement can be achieved after the implementation of multibiometrics. The comparative experiments also ascertain that the proposed fusion approach outperforms the state-of-art verification performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.892-911
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• 2019

Biometric recognition systems have been widely used for information security. Among the most popular biometric traits, there are fingerprint and face due to their high recognition accuracies. However, the security system that uses face recognition as the login method are vulnerable to face-spoofing attacks, from using printed photo or video of the valid user. In this study, we propose a fast and robust method to detect face-spoofing attacks based on the analysis of spatial frequency differences between the real and fake videos. We found that the effect of a spoofing attack stands out more prominently in certain regions of the 2D Fourier spectra and, therefore, it is adequate to use the information about those regions to classify the input video or image as real or fake. We adopt a divide-conquer-aggregate approach, where we first divide the frequency domain image into local blocks, classify each local block independently, and then aggregate all the classification results by the weighted-sum approach. The effectiveness of the methodology is demonstrated using two different publicly available databases, namely: 1) Replay Attack Database and 2) CASIA-Face Anti-Spoofing Database. Experimental results show that the proposed method provides state-of-the-art performance by processing fewer frames of each video.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.303-316
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• 2021

Digital content protection has recently become an important requirement in biometrics-based authentication systems due to the challenges involved in designing a feasible and effective user authentication method. Biometric approaches are more effective than traditional methods, and simultaneously, they cannot be considered entirely reliable. This study develops a reliable and trustworthy method for verifying that the owner of the biometric traits is the actual user and not an impostor. Watermarking-based approaches are developed using a combination of a color face image of the user and a mobile equipment identifier (MEID). Employing watermark techniques that cannot be easily removed or destroyed, a blind image watermarking scheme based on fast discrete curvelet transform (FDCuT) and discrete cosine transform (DCT) is proposed. FDCuT is applied to the color face image to obtain various frequency coefficients of the image curvelet decomposition, and for high frequency curvelet coefficients DCT is applied to obtain various frequency coefficients. Furthermore, mid-band frequency coefficients are modified using two uncorrelated noise sequences with the MEID watermark bits to obtain a watermarked image. An analysis is carried out to verify the performance of the proposed schema using conventional performance metrics. Compared with an existing approach, the proposed approach is better able to protect multimedia data from unauthorized access and will effectively prevent anyone other than the actual user from using the identity or images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.12
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• pp.6133-6151
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• 2017

The field of periocular biometrics has gained wide attention as an alternative or supplemental means to conventional biometric traits such as the iris or the face. Periocular biometrics provide intermediate resolution between the iris and the face, which enables it to support both. We have developed a periocular recognition system by using uniform Multiscale Local Binary Pattern (uMLBP) and attribute features. The proposed system has been evaluated in terms of major factors that need to be considered on a mobile platform (e.g., distance and facial pose) to assess the feasibility of the use of periocular biometrics on mobile devices. Experimental results showed 98.7% of rank-1 identification accuracy on a subset of the Face Recognition Grand Challenge (FRGC) database, which is the best performance among similar studies.

• Smart Media Journal
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• v.9 no.2
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• pp.39-50
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• 2020

Derivation cryptographic keys from human biometrics opens a new promising research area when it can be used efficiently for not only verification or recognition tasks, but also symmetric-key based applications. Among existing biometric traits, face is considered as the most popular biometrics since facial features are informative and discriminative. In this paper, we present a comprehensive survey of Face-based key generation (FKGS). First, we summarize the trend of FKGS researches and sum up the methods which play important roles in the proposed key generation systems. Then we present the evaluation and the general performance analysis; from that, we give a discussion about the advantages and disadvantages of surveyed studies to clarify the fundamental requirements and the main challenges when implementing FKGS in practice. Finally, an outlook on future prospects is given.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.4
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• pp.593-601
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• 2017

Objective: The aim of this study was to evaluate heat tolerance using heat tolerance indices, physiological, physical, thermographic, and hematological parameters in Santa Ines and Morada Nova sheep breeds in the Federal District, Brazil. Methods: Twenty-six adult hair sheep, one and a half years old, from two genetic groups (Santa Ines: 12 males and 4 females; Morada Nova: 7 males and 3 females) were used and data (rectal temperature, respiratory rate, heart rate, skin temperatures; hematological parameters) were collected during three consecutive days, twice a day (morning and afternoon), with a total of six repetitions. Also physical parameters (biometric measurements, skin and hair traits) and heat tolerance indices (temperature-humidity index, Iberia and Benezra) were evaluated. The analyses included analyses of variance, correlation, and principal components with a significance level of 5%. Results: The environmental indices, in general, indicate a situation of thermal discomfort for the animals during the afternoon. Breed significantly influenced (p<0.001) physiological and physical characteristics of skin, hair, biometric measurements and Iberia and Benezra heat tolerance indices. Santa Ines animals were bigger and had longer, greater number and darker hair, thicker skin, greater respiratory rate and Benezra index and lower Iberia index compared with Morada Nova breed. Conclusion: Although both breeds can be considered adapted to the environmental conditions of the region, Morada Nova breed is most suitable for farming in the Midwest region. The positive correlation found between the thermographic temperatures and physiological parameters indicates that this technique can be used to evaluate thermal comfort. Also, it has the advantage that animals do not have to be handled, which favors animal welfare.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.8_spc
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• pp.1275-1283
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• 2019

Goats (Capra hircus) were domesticated during the late Neolithic, approximately 10,500 years ago, and humans exerted minor selection pressure until fairly recently. Probably the largest genetic change occurring over the millennia happened via natural selection and random genetic drift, the latter causing genes to be fixed in small and isolated populations. Recent human-influenced genetic changes have occurred through biometrics and genomics. For the most part, biometrics has concentrated upon the refining of estimates of heritabilities and genetic correlations. Heritabilities are instrumental in the calculation of estimated breeding values and genetic correlations are necessary in the construction of selection indices that account for changes in multiple traits under selection at one time. Early genomic studies focused upon microsatellite markers, which are short tandem repeats of nucleic acids and which are detected using polymerase chain reaction primers flanking the microsatellite. Microsatellite markers have been very important in parentage verification, which can impact genetic progress. Additionally, microsatellite markers have been a useful tool in assessing genetic diversity between and among breeds, which is important in the conservation of minor breeds. Single nucleotide polymorphisms are a new genomic tool that have refined classical BLUP methodology (biometric) to provide more accurate genomic estimated breeding values, provided a large reference population is available.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.305-314
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• 2017

Recently, user authentication through biometric traits such as fingerprint and iris raise more and more attention especially in mobile commerce and fin-tech fields. In particular, commercialized authentication methods using fingerprint recognition are widely utilized mainly because customers are more adopted and used to fingerprint recognition applications. In the meantime, the security issues caused by fingerprint falsification bring lots of attention. In this paper, we propose a new method to improve the performance of fake fingerprint detection using CNN(Convolutional Neural Network). It is common practice to increase the amount of learning data by using affine transformation or horizontal reflection to improve the detection rate in CNN characteristics that are influenced by learning data. However, in this paper we propose an effective data augmentation method based on the database difficulty level. The experimental results confirm the validity of proposed method.