Stable Biometric Features Description not definition Biometric features

Stable Biometric Features Description (not definition): Biometric features whose value change very infrequently among multiple prints of a finger Deformation Invariant Features V/S Stable Features: Since biometrics are prone to burst errors in addition to noise and other deformations due to unavoidable conditions so only deformation (linear and nonlinear) invariant features won’t suffice to implement total invariance.

Fingerprints from same finger Deformation invariant features Stable Features

Stable Feature Extraction n n Element by element quantization Using the error correcting codes to counter burst errors.

Element by element quantization n n(~10 -15) sample features from prints of same finger are taken at the registration step Mean and variance of each feature element is calculated over the samples Lower and upper bounds on the variance is set to take care of extreme situations q Clustering of the samples could also be done to handle the burst errors as error-free samples would cluster out

n n n The possible range of feature values i. e. 0255 is divided into blocks of width 6σ such that the mean is at the center of the block. Any value of a particular feature element is quantized to the center of the block in which it lies. The block-length of each division of the range(0 -255) for each element and the offset of the first block from 0 is made public for quantization.

Feature Elements For each element n samples 0 μ 6σ Mean (μ) SD (σ) 255

Using Error-correcting codes for stability n n n A new scheme has been designed to utilize the error correcting codes for stability The mean vector of the sample features is taken as the quantized feature vector. This vector is assumed to be a RS error correcting code of certain desired error correcting capability. The vector is decoded to get the message The message is again coded to get the error free message.

Mean (μ) RS decode Decoded message RS encode Error free code n Since the range of values is fixed(0 -255) a cyclic shift map is found from the quantized feature vector (mean) to the error free code. Mean (μ) Cyclic shift map Error free code n The cyclic shift map is made public

Extracting the stable feature n n First the feature vector is quantized using the blocklength and the offset The quantized feature vector is transformed using the cyclic shift map and decoded to get the stable feature. Feature Vector Quantization Quantized Feature Vector Cyclic shift map Shifted Vector RS decode Stable Feature