Intelligent Computing for Automated Biometrics, Criminal and Forensic Applications

Choraś, Michał

doi:10.1007/978-3-540-74171-8_119

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…There are already several palmprint based methods that showed high accuracy in representing the identity of each individual [1][2][3].…”

Section: Related Workmentioning

confidence: 99%

Contactless palmprint and knuckle biometrics for mobile devices

Choraś¹,

Kozik²

2011

Pattern Anal Applic

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In this paper, biometric methods for contactless and unrestricted access control for mobile devices are proposed. The major contribution of this paper are palmprint and knuckles feature extraction methods dedicated for the mobile contactless biometrics. We use texture maskbased features for the palmprint. For the knuckles, we use Probabilistic Hough Transform and Speeded Up Robust Features as well as the 3-step classification methodology. We prove the efficiency of the presented methods by reporting promising results.

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“…There are already several palmprint based methods that showed high accuracy in representing the identity of each individual [1][2][3].…”

Section: Related Workmentioning

confidence: 99%

Contactless palmprint and knuckle biometrics for mobile devices

Choraś¹,

Kozik²

2011

Pattern Anal Applic

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“…The outer ear also differs from other biometrics, such as the face, since the face can be affected by facial expression, ageing, wearing sunglasses, makeup effects, and growing or shaving beard [15]. Furthermore, the ear size is bigger compared to the size of iris, retina, and fingerprint, so it is likewise readily capturing from a distance [16,17]. The outer ear and its important features have been described by several researchers since it has a significant role in the identification of victims of crimes and criminals or accidents surveillance video, and authentications [18,19].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Ear Recognition Technique

Hussein¹,

Sahan²

2021

IJASCA

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The human ear has unique and attractive details; therefore, human ear recognition is one of the most important fields in the biometric domains. In this work, we proposed an efficient and intelligent ear recognition technique based on particle swarm optimization, discrete wavelet transform, and fuzzy neural network. Discrete wavelet transform is used to provide comprise and effective features about the ear image, while the particle swarm optimization utilized to select more effective and attractive features. Furthermore, using particle swarm optimization leads to reduce the complexity of the classification stage since it reduces the number of the features. Fuzzy neural network used in the classification stage in order to provide strong distinguishing between the testing and training ear images. many experiments performed using two ear databases to examine the accuracy of the proposed technique. The analysis of the results refers that the presented technique gained high recognition accuracy using various data sets with less complexity. Keywords: Ear recognition; bio-metric; discrete wavelet transform, particle swarm optimization, fuzzy neural network.

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“…Ear is relatively easy to extract, since its immediate background is predictable, moreover it can be easily captured without a fully cooperative subject, without requiring particular hygiene (unlike fingerprints) or collaboration (unlike iris and retina). According to the evaluations performed by polish police [1] the ear extraction is not only a highly accepted biometrics but also more comfortable with respect to the face images enrolment.…”

Section: Introductionmentioning

confidence: 99%