1,920 research outputs found
Iris Recognition Using Scattering Transform and Textural Features
Iris recognition has drawn a lot of attention since the mid-twentieth
century. Among all biometric features, iris is known to possess a rich set of
features. Different features have been used to perform iris recognition in the
past. In this paper, two powerful sets of features are introduced to be used
for iris recognition: scattering transform-based features and textural
features. PCA is also applied on the extracted features to reduce the
dimensionality of the feature vector while preserving most of the information
of its initial value. Minimum distance classifier is used to perform template
matching for each new test sample. The proposed scheme is tested on a
well-known iris database, and showed promising results with the best accuracy
rate of 99.2%
Fingerprint Recognition Using Translation Invariant Scattering Network
Fingerprint recognition has drawn a lot of attention during last decades.
Different features and algorithms have been used for fingerprint recognition in
the past. In this paper, a powerful image representation called scattering
transform/network, is used for recognition. Scattering network is a
convolutional network where its architecture and filters are predefined wavelet
transforms. The first layer of scattering representation is similar to sift
descriptors and the higher layers capture higher frequency content of the
signal. After extraction of scattering features, their dimensionality is
reduced by applying principal component analysis (PCA). At the end, multi-class
SVM is used to perform template matching for the recognition task. The proposed
scheme is tested on a well-known fingerprint database and has shown promising
results with the best accuracy rate of 98\%.Comment: IEEE Signal Processing in Medicine and Biology Symposium, 201
3D Face Recognition using Significant Point based SULD Descriptor
In this work, we present a new 3D face recognition method based on Speeded-Up
Local Descriptor (SULD) of significant points extracted from the range images
of faces. The proposed model consists of a method for extracting distinctive
invariant features from range images of faces that can be used to perform
reliable matching between different poses of range images of faces. For a given
3D face scan, range images are computed and the potential interest points are
identified by searching at all scales. Based on the stability of the interest
point, significant points are extracted. For each significant point we compute
the SULD descriptor which consists of vector made of values from the convolved
Haar wavelet responses located on concentric circles centred on the significant
point, and where the amount of Gaussian smoothing is proportional to the radii
of the circles. Experimental results show that the newly proposed method
provides higher recognition rate compared to other existing contemporary models
developed for 3D face recognition
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