Study on biometric authentication systems, challenges and future trends: A review

Dharavath, Krishna; Talukdar, Fazal A.; Laskar, Rabul Hussain

doi:10.1109/iccic.2013.6724278

Cited by 57 publications

(18 citation statements)

References 9 publications

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“…This is used to prevent spam and autoregistration of new accounts for a website or email. The use of biometric systems [9] is another form of challengeresponse authentication. In cryptography, zeroknowledge password proof and key agreement systems such as secure remote password, CRAM-MD5 and secure shell's challenge-response system based on RSA are considered to be use of very advanced challenge-response algorithms.…”

Section: A Challenge Response Authenticationmentioning

confidence: 99%

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Study on the Effectiveness of Spam Detection Technologies

Iqbal¹,

Abid²,

Ahmad³

et al. 2016

IJITCS

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Abstract-Nowadays, spam has become serious issue for computer security, because it becomes a main source for disseminating threats, including viruses, worms and phishing attacks. Currently, a large volume of received emails are spam. Different approaches to combating these unwanted messages, including challenge response model, whitelisting, blacklisting, email signatures and different machine learning methods, are in place to deal with this issue. These solutions are available for end users but due to dynamic nature of Web, there is no 100% secure systems around the world which can handle this problem. In most of the cases spam detectors use machine learning techniques to filter web traffic. This work focuses on systematically analyzing the strength and weakness of current technologies for spam detection and taxonomy of known approaches is introduced.

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Section: A Challenge Response Authenticationmentioning

confidence: 99%

“…Although most of the recent computer networks are being properly designed and segmented accordingly, however, still there is a risk of targeted spam attacks. In order to protect from un-wanted traffic like spam, it is significant to know which endpoint and network based cyber security controls are available [9,10].…”

Section: Introductionmentioning

confidence: 99%

Study on the Effectiveness of Spam Detection Technologies

Iqbal¹,

Abid²,

Ahmad³

et al. 2016

IJITCS

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“…Um Sistema Biométrico Unimodal baseado em face pode ser descrito como um sistema que usa uma câmera fotográfica para capturar uma imagem contendo a face do usuário e então verifica se o usuário está ou não cadastrado no sistema. Esteé considerado um sistema de baixo custo de construção, já que qualquer câmera pode ser utilizada para capturar a face, porém quanto maior a resolução da imagem, mais informação pode ser extraída, facilitando o reconhecimento do usuário [6].…”

Section: Sistemas Biométricos Unimodaisunclassified

“…Na Figura 2 temos um exemplo das faces detectadas e segmentadas. Um dos grandes desafios enfrentados pelos Sistemas Biométricos baseados em Face está na detecção da face do usuário em ambientes com uma grande variação de iluminação, poses ou com a presença de acessórios [6]. Devido a essas limitações, os Sistemas Biométricos baseados em face são geralmente utilizados em ambientes controlados, que são locais onde esses algoritmos funcionam perfeitamente.…”

Section: Sistemas Biométricos Unimodaisunclassified

Um estudo comparativo das estratégias de fusão no nível de característica para Sistemas Biométricos Multimodais baseados em face e íris

Costa¹,

Passos²,

Peres³

et al. 2015

Anais Do Simpósio Brasileiro De Sistemas De Informação (SBSI)

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Com o avanço da tecnologia, novas abordagens para o reconhecimento automático da identidade de uma pessoa têm sido propostas e tal fato tem encorajado o emprego de Sistemas Biométricos. Essa abordagem utiliza características físicas ou comportamentais de uma pessoa para realizar a sua identificação. Os Sistemas Biométricos podem ser classificados como Unimodais ou Multimodais. Sistemas Biométricos Unimodais utilizam apenas uma modalidade biométrica para realizar o reconhecimento, ao passo que os Sistemas Biométricos Multimodais empregam duas ou mais modalidades. A construção de um Sistema Biométrico Multimodal pode ser realizada de diferentes formas, as quais são categorizadas de acordo com a sua arquitetura, nível de fusão e estratégia de fusão. O objetivo deste trabalho é investigar diferentes formas de fusão de modalidades biométricas no nível de característica, visando projetar um sistema multimodal com alto poder de reconhecimento. Neste artigo, nós utilizamos a Transformada Wavelet para extrair um conjunto de características de imagens de face e íris. Os resultados obtidos mostram que os Sistemas Biométricos Multimodais apresentam melhor desempenho que os Sistemas Biométricos Unimodais, em termos de taxa de reconhecimento calculada sobre a saída produzida pela Máquina de Vetores Suporte usada como classificador.

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“…Since the cost of implementing the technology is high [7], it is more practical to improve the algorithm instead of the device used to capture the images. Therefore, Sahmoud and Abuhaiba [8] stated to increase the visibility of pupil; a contrast enhancement technique is needed.…”

mentioning

confidence: 99%

Image Enhancement Technique at Different Distance for Iris Recognition

Hassan

Kasim

Jafery

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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Capturing eye images within visible wavelength illumination in the non-cooperative environment lead to the low quality of eye images. Thus, this study is motivated to investigate the effectiveness of image enhancement technique that able to solve the abovementioned issue. A comparative study has been conducted in which three image enhancement techniques namely Histogram Equalization (HE), Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) were evaluated and analysed. UBIRIS.v2 eye image database was used as a dataset to evaluate those techniques. Moreover, each of enhancement techniques was tested against the different distance of eye image captured. Results were compared in terms of image interpretation by using Peak-Signal Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Mean Absolute Error (MAE). The effectiveness of the enhancement techniques on the different distance of image captured was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR). As a result, CLAHE has proven to be the most reliable technique in enhancing the eye image which improved the localization accuracy by 7%. In addition, the results showed that by implementing CLAHE technique at a four-meter distance was an ideal distance to capture eye images in a non-cooperative environment where it provides high recognition accuracy, 74%.

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Study on biometric authentication systems, challenges and future trends: A review

Cited by 57 publications

References 9 publications

Study on the Effectiveness of Spam Detection Technologies

Study on the Effectiveness of Spam Detection Technologies

Um estudo comparativo das estratégias de fusão no nível de característica para Sistemas Biométricos Multimodais baseados em face e íris

Image Enhancement Technique at Different Distance for Iris Recognition

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