Palmprint recognition using Kekre's wavelet's energy entropy based feature vector

Kekre, H. B.; Bharadi, Vinayak Ashok; Singh, Vikas; Ambardekar, Amol

doi:10.1145/1980022.1980031

Cited by 3 publications

(2 citation statements)

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“…We have used Kekre"s Wavelet for extraction of feature vector and the palmprint was decomposed into five levels. For classification relative wavelet energy entropy as well as Euclidian distance based classifier is used [20].…”

Section: Existing Methodsmentioning

confidence: 99%

“…The DC component, separate means of the Sal and Cal component and the last sequency component together form the feature vector, and hence the number of features is 2S+2, where S is the number of blocks. We have selected 192*192 pixels size region of interest for the palmprint as discussed by Kekre & Bharadi [31]. The Fingerprint ROI is 144*144 pixels in size, The Finger-knuckle print ROI is 256*128 pixels size.…”

Section: Complex Walsh Plane [27] and Feature Vector Generationmentioning

confidence: 99%

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Texture Feature Extraction For Biometric Authentication using Partitioned Complex Planes in Transform Domain

Bharadi¹

2012

SpecialIssue

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Abstract-Feature vector generation is an important step in biometric authentication. Biometric traits such as fingerprint, finger-knuckle prints, palmprint, and iris are rich in texture. This texture is unique and the feature vector extraction algorithm should correctly represent the texture pattern. In this paper a texture feature extraction methodology is proposed for these biometric traits. This method is based on one step transform of the two dimensional images and then using the intermediate transformation data to generate complex planes for feature vector generation. This method is implemented using Walsh, DCT, Hartley, Kekre Transform & Kekre Wavelets. Results indicate the effectiveness of the feature vector for biometric authentication.

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Section: Existing Methodsmentioning

confidence: 99%

Section: Complex Walsh Plane [27] and Feature Vector Generationmentioning

confidence: 99%