Biometric Identifiers Research Paper

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1. INTRODUCTION 1.1 Biometric Technology: Biometrics refers to the identification or authentication of an individual based on certain unique features or characteristics. Biometric identifiers are the distinctive and measurable features that is used to label and describe individuals. There are two categories of biometric identifiers namely physiological and behavioural characteristics. Iris, fingerprint, DNA, etc. belong to the former kind of biometric identifiers whereas typing rhythm, gait, voice, etc. belong to the latter. A biometric system usually functions by first capturing a sample of the feature, such as capturing a digital colour image of a face to be used in facial recognition or a recording a digitized sound signal to be used in …show more content…

Pattern recognition 2. Segmentation(threshold based segmentation) 3. Feature extraction 4. BSIF (Binarized Statistical Image Feature) 2.1Pattern recognition: Iris imaging requires use of a high quality digital camera. Today’s commercial iris cameras typically use infrared light to illuminate the iris without causing harm or discomfort to the subject. Upon imaging an iris, a 2D Gabor wavelet filters and maps the segments of the iris into phasors (vectors). These phasors include information on the orientation and spatial frequency (“what” of the image) and the position of these areas (“where” of the image). This information is used to map the Iris Codes. 2.2 Segmentation: Partitioning an image into regions corresponding to objects. All pixels in a region share a common property .Simplest property that pixels can share intensity Thresholding=separation of light and dark regions In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain …show more content…

Feature Extraction: In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. Feature extraction is related to dimensionality reduction. When the input data to an algorithm is too large to be processed and it is suspected to be redundant (e.g. the same measurement in both feet and meters, or the repetitiveness of images presented as pixels), then it can be transformed into a reduced set of features (also named a features vector). This process is called feature extraction. The extracted features are expected to contain the relevant information from the input data, so that the desired task can be performed by using this reduced representation instead of the complete initial data. Figure.2.3.Detecting an object (left) in a cluttered scene (right) using combination feature detection, feature extraction and matching. See example for

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