THE IDENTIFICATION OF EAR PRINTS USING COMPLEX GABOR FILTERS
Abstract: Biometrics is a
method used to recognize humans based on one or a few characteristics physical
or behavioral traits that are unique such as DNA, face, fingerprints, gait,
iris, palm, retina, signature and sound. Although the facts that ear prints are
found in 15% of crime scenes, ear prints research has been very limited since
the success of fingerprints modality. The advantage of the use of ear prints,
as forensic evidence, are it relatively unchanged due to increased age and have
fewer variations than faces with expression variation and orientation. In this
research, complex Gabor filters is used to extract the ear prints feature based
on texture segmentation. Principal componentanalysis (PCA) is then used for
dimensionality-reduction where variation in the dataset is preserved. The
classification is done in a lower dimension space defined by principal
components based on Euclidean distance. In experiments, it is used left and
right ear prints of ten respondentsand in average, the successful recognition
rate is 78%. Based on the experiment results, it is concluded that ear prints
is suitable as forensic evidence mainly when combined with other biometric
modalities.
Keywords: Biometrics; Ear
prints; Complex Gabor filters; Principal component analysis; Euclidean distance
Author: Alexander A S Gunawan,
Heni Kurniaty, Wikaria Gazali
Journal Code: jptinformatikagg120010
