Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Recognition by Linear Combinations of Models
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
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Authors have developed a novel method for achieving higher recognition capability of a 3-D face recognition system based on feature line method. This method, which is called Modified Nearest Feature Line, has used as a classifier and it is combined with our developed Subspace Karhunen-Loeve transformation method as a feature extraction system to build a 3-D face recognition system. In this paper, the authors evaluated and analyzed the performance of this Modified Nearest Feature Line method for recognizing 3-D face images with various numbers of objects. As recognition rate is usually decreases by increasing the number of objects to be recognized, the performance of M-NFL method, in its recognition rate, is evaluated and then compare with that of the conventional NFL method. Experimental results show that the use of various numbers of objects influenced the recognition rate of the both two system, however, the slope of the decrement value using M-NFL method is lower than NFL method. It is also shown that in every same number of objects to be recognized, M-NFL method always gave a high recognition rate than the NFL method, with up to 20% in recognition rate difference value.