Title :
Evaluation of GMM approach to fingerprint classification
Author :
Cotrina-Atencio, Anibal ; Samatelo, Jorge L A ; Salles, Evandro O T
Author_Institution :
PPGEE, Univesidade Fed. do Esprito Santo, Vitoria, Brazil
Abstract :
This paper investigates the modeling of the characteristic vector of a PCASYS approach to fingerprint classification problem. In a previous work, it is proposed a new algorithm based in multiple levels of representation to detect the reference points in a fingerprint. The results indicates that an unimodal Gaussian distribution models each of the fingerprint classes, in contrast of other results that indicates a Perceptron neural network as the best classifier. Therefore, in order to verify it, this paper suggests more accurate tests over the feature vector. Here, each class is supposed unknown and modeled by two approaches: GMM (Gaussian Mixture Model), classified by Normal classifier, and a Gaussian Mixture Based Classifier (GMBC). The tests are conducted using the DB4 database and the protocol suggested by the National Institute of Standards and Technology (NIST). Finally, the results are evaluated and discussed at the end of the paper.
Keywords :
feature extraction; fingerprint identification; image classification; image representation; medical image processing; neural nets; DB4 database; GMM approach; Gaussian mixture based classifier; Gaussian mixture model; National Institute of Standards and Technology; PCASYS approach; feature vector; fingerprint classification; image representation; normal classifier; perceptron neural network; reference points; unimodal Gaussian distribution model; Artificial neural networks; Clustering algorithms; Covariance matrix; Error analysis; Gaussian distribution; NIST; Principal component analysis;
Conference_Titel :
Biosignals and Biorobotics Conference (BRC), 2011 ISSNIP
Conference_Location :
Vitoria
Print_ISBN :
978-1-4244-8212-2
DOI :
10.1109/BRC.2011.5740662