UPSI Digital Repository (UDRep)
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Abstract : Universiti Pendidikan Sultan Idris |
Current identification of passive detection has been attention in the modern world due to the
system's robustness as an ear recognition framework based on a multiclassifier and attempt to
create user patterns via extracted features from ear images, which have unique individual
identities. The collected features from the ear intersection points and the angles bounded
between curves using different descriptors and classifiers are considered unique information
used to generate unique features. The proposed framework commenced with the extraction of
eight sets of features (LBP, BSIF, LPQ, RILPQ, POEM, HOG, DSIFT, and Gabor) from 2D
ear images. Subsequently, ELM and SVM classifiers were trained on each set of features.
Seven combination rules (MR, AR, GWAR, ICWAR, Borda, DS, and AV (GWAR, Borda,
DS)) were utilized to acquire a total of 16 classifiers. Also, two optimization rules; genetic
algorithm and brute force were proposed for accuracy enhancement. The AWE and the USTB
datasets were utilized in the development, evaluation, and validation of an ear recognition
framework dataset. So, some vulnerabilities are observed in datasets and all challenges for ear
biometrics. The research findings showed that combining classifiers using different sets of
features yields better performance compared to using individual classifiers. However, using
one classifier or limited number is not enough to solve the problem of ear recognition with
different challenges such as Pose, Occlusion, Illumination, Blurry image, Rotation, Lighting,
Scale, and Translation. The validation of such a framework using the AWE dataset showed that
the SVM and ELM in combination with modern descriptors managed to enhance the
recognition. Rank-1 accuracy also reached 99% with Genetic Algorithm optimization, and 98%
with brute-force AR and brute-force GWAR. These results are compared to other results in the
literature and found to be superior. In conclusion, the main findings showed that the proposed
framework consisting of two classifiers SVM and ELM trained with selected features and the
combination rules managed to attain higher accuracy in-ear recognition compared with
previous studies. This ear recognition framework is a major step towards the recognition of
individuals from ears in real-world conditions. This study implies that the proposed ear
recognition framework based on ELM and SVM classifiers with combination and optimization
rules can be utilized to improve the effectiveness of passive human recognition where security
is of utmost importance. |
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