Multimodal Biometric Authentication System Based on Hand Features

Pavešić, Nikola; Savič, Tadej; Ribarić, Slobodan

doi:10.1007/3-540-31314-1_77

Cited by 5 publications

(1 citation statement)

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“…In particular, hand and face recognitions [ 25 , 26 ] are commonly used together for biometric authentication. A study in [ 27 ] advocates using a multimodal biometric authentication system that is based solely on features of a human hand. The tri-modal system takes information from Eigen-coefficients of palm, fingers between first and third phalanx, and finger tips.…”

Section: Related Workmentioning

confidence: 99%

A biometric authentication model using hand gesture images

Fong¹,

Zhuang²,

Fister

2013

BioMed Eng OnLine

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A novel hand biometric authentication method based on measurements of the user’s stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information, associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password ‘iloveu’ in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, ‘i’ , ‘l’ , ‘o’ , ‘v’ , ‘e’ , and ‘u’. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. It is believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy of this novel biometric authentication model which shows up to 93.75% recognition accuracy.

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Section: Related Workmentioning

confidence: 99%