الفهرس 
BiometricsOnly 14 pages are availabe for public view
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Abstract
Accurate automatic person identification is critical in a variety
of applications in our electronically interconnected society.
Biometrics, which refers to identification based on physical or
behavioral characteristics, is being increasingly adopted to provide
positive identification with a high degree of confidence.
Among all the biometric techniques, fingerprint-based
authentication systems have received the most attention because of
the long history of fingerprints and their extensive use in forensics.
Fingerprints were one of the first forms of biometric authentication
to be used for low enforcement and civilian applications. Contrary
to popular belief and despite decades of research in fingerprints, a
reliable fingerprint is still an open problem.
In this thesis, we present some contribution to advance the state
of the art methods in this field. First, quality enhancement of
fingerprint images is important for a good performance of an
Automatic Fingerprint Identification System (AFIS). So a hybrid
fingerprint enhancement algorithm is presented. This algorithm is
based on morphological enhancement in the additive wavelet
transform domain and Wave Atom denoising. The performance of
the proposed algorithm has been evaluated on a set of images. The
results are compared with the results obtained using morphological
enhancement only. Our proposed algorithm has given a better
performance than using morphological enhancement only.
The second contribution of the thesis addresses the problem of
fingerprint identification. It presents a new fingerprint identification
method based on Mel Frequency Cepstral Coefficients (MFCCs).
This approach is based on treating the problem as a pattern
recognition problem. Cepstral features are extracted from a group of
fingerprint images, which are transformed first to 1-D signals by
lexicographic ordering. MFCCs and polynomial shaping coefficients
are extracted from these 1-D signals to form a database of features,
which can be used to train a neural network. In the testing phase of
this neural network, fingerprint identification is performed. The last
contribution of the thesis presents a study for the sensitivity of the
proposed cepstral approach for fingerprint recognition to synthetic
pixels obtained through interpolation. The objective of this study is
to allow fingerprint database compression through decimation.