Finger Knuckle Print and Palmprint for efficient person recognition

El-Tarhouni, Wafa (2017) Finger Knuckle Print and Palmprint for efficient person recognition. Doctoral thesis, Northumbria University.

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Abstract

Biometric person recognition systems are increasingly being used to enhance the security of physical and logical security systems. Palmprint and finger knuckle print recognition have gained attention in research and practical domains, providing a means of identification for security system access and personal recognition and presenting an interesting and challenging research problem. The overall aim of this work is to investigate biometric systems able to recognise people using their palmprints and finger knuckle prints. The work investigates the theoretical concepts behind palmprint and finger knuckle print recognition and proposes new algorithms to extract features for recognition systems able to identify a person from a test sample with a strong degree of confidence. The research has led to five contributions.

The first contribution is concerned with the development of an ensemble learning framework using a variant of local binary patterns constructed from Pascal's coefficients of order n, termed Pascal's coefficient multiscale local binary pattern. In addition, a feature extraction technique which combines pyramid histograms of oriented gradients and Pascal's coefficient local binary patterns by concatenating the features for use in classification is also proposed. Secondly, a fusion approach is proposed by combining local binary pattern histograms of Fourier features with Gabor filter technique to generate a single feature extraction to improve palmprint recognition. The third contribution is related to a novel feature extraction method applied for use in palmprint and Finger Knuckle Print recognition. The multi-shift local binary pattern approach extends the original shift local binary pattern concept to a multi-scale dimension to obtain more robust and discriminating feature representations by extracting histograms and concatenating them into a single feature vector. The fifth contribution proposes a novel Fibonacci sequence local binary pattern descriptor and multi-scale Fibonacci sequence local binary pattern descriptor by carefully modifying the operator thresholding scheme at the pixel values. To achieve this Fibonacci numbers have been used to generate a distribution of binary codes at every pixel position in order to create descriptors that are more robust against lighting variations of images. Finally, a new feature set is developed for finger knuckle print recognition. This is inspired by using the completed local binary pattern, termed the dynamic threshold CLBP, which employs only the sign and magnitude components. The novelty proposes to encode the magnitude features using a dynamic thresholding technique to concatenate the sign and magnitude features.

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