Assessment of predictive clustering trees on 2D-image-based Ear recognition
Journal
International Electrotechnical and Computer Science Conference
Date Issued
2016
Author(s)
Emeršic, Žiga
Peer, Peter
Abstract
In the last decade person recognition based on various
biometric metrics have steadily been gaining on popularity. The same holds for machine learning approaches
and various image classification and retrieval techniques.
However, many techniques rely on distinguishing between
significantly dissimilar images, which is often not the case
in person recognition. Person recognition based on images relies on detecting minor differences and not global
appearance of an image. To test if retrieval approaches
based on bag-of-words fail in the task of biometric recognition we evaluated the following procedure. Ear images
were used to extract Scale Invariant Feature Transform
feature vectors. These vectors were then fed into forest of
Predictive Clustering Trees, k-means and approximate kmeans; and then compared to baseline system where only
distances between plain descriptors are compared. While
these methods have been proven to perform well in image
with significantly different content, the results show that
these methods do not perform well under the task of ear
recognition.
biometric metrics have steadily been gaining on popularity. The same holds for machine learning approaches
and various image classification and retrieval techniques.
However, many techniques rely on distinguishing between
significantly dissimilar images, which is often not the case
in person recognition. Person recognition based on images relies on detecting minor differences and not global
appearance of an image. To test if retrieval approaches
based on bag-of-words fail in the task of biometric recognition we evaluated the following procedure. Ear images
were used to extract Scale Invariant Feature Transform
feature vectors. These vectors were then fed into forest of
Predictive Clustering Trees, k-means and approximate kmeans; and then compared to baseline system where only
distances between plain descriptors are compared. While
these methods have been proven to perform well in image
with significantly different content, the results show that
these methods do not perform well under the task of ear
recognition.
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