Automated fingerprint recognition using structural matching
Pattern Recognition
On-Line Fingerprint Verification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Image Enhancement: Algorithm and Performance Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
Neural Network Theory
Neural Networks
Auditory-Based Wavelet Packet Filterbank for Speech Recognition Using Neural Network
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Face active appearance modeling and speech acoustic information to recover articulation
IEEE Transactions on Audio, Speech, and Language Processing - Special issue on multimodal processing in speech-based interactions
IEEE Transactions on Audio, Speech, and Language Processing
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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.