Fingerprint Recognition Using Mel-Frequency Cepstral Coefficients

This paper presents a new fingerprint recognition method based on mel-frequency cepstral coefficients (MFCCs). In this method, cepstral features are extracted from a group of fingerprint images, which are transformed first to 1-D signals by lexicographic ordering. MFCCs and polynomial shape coefficients are extracted from these 1-D signals or their transforms to generate a database of features, which can be used to train a neural network. The fingerprint recognition can be performed by extracting features from any new fingerprint image with the same method used in the training phase. These features are tested with the neural network. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral method for fingerprint recognition at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction. © 2010 Pleiades Publishing, Ltd.