Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Keyword Spotting in Poorly Printed Documents using Pseudo 2-D Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Teeth Image Recognition for Biometrics
IEICE - Transactions on Information and Systems
An embedded HMM-based approach for face detection and recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Journal of Cognitive Neuroscience
Improved PCA-Based Personal Identification Method Using Invariance Moment
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Multimodal biometric authentication using teeth image and voice in mobile environment
IEEE Transactions on Consumer Electronics
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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Most traditional biometric approaches generally utilize a single image for personal identification. However, these approaches sometimes failed to recognize users in practical environment due to false-detected or undetected subject. Therefore, this paper proposes a novel recognition approach based on multiple frame images that are implemented in mobile devices. The aim of this paper is to improve the recognition accuracy and to reduce computational complexity through multiple attempts. Here, multiple attempts denote that multiple frame images are used in time of recognition procedure. Among sequential frame images, an adequate subject, i.e., teeth image, is chosen by subject selection module which is operated based on differential image entropy. The selected subject is then utilized as a biometric trait of traditional recognition algorithms including PCA, LDA, and EHMM. The performance evaluation of proposed method is performed using two teeth databases constructed by a mobile device. Through experimental results, we confirm that the proposed method exhibits improved recognition accuracy of about 3.6-4.8%, and offers the advantage of lower computational complexity than traditional biometric approaches.