Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
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WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
Automatic Features Detection for Overlapping Face Images on their 3D Range Models
ICIAP '01 Proceedings of the 11th International Conference on Image Analysis and Processing
A Feature-Based Face Recognition System
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
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ACM Computing Surveys (CSUR)
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AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
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IEEE Transactions on Neural Networks
Decision-based neural networks with signal/image classification applications
IEEE Transactions on Neural Networks
Experiments on lattice independent component analysis for face recognition
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
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Face recognition is the identification of humans by the unique characteristics of their faces and forms the basis for many biometric systems. In this research the problem of feature-based face recognition is considered. Bidimensional regression (BDR) is an extension of standard regression to 2D variables. Bidimensional regression can be used to determine the degree of resemblance between two planar configurations of points and for assessing the nature of their geometry. A primary advantage of this approach is that no training is needed. The goal of this research is to explore the suitability of BDR for 2D matching. Specifically, we explore if bidimensional regression can be used as a basis for a similarity measure to compare faces. The approach is tested using standard datasets. The results show that BDR can be effective in recognizing faces and hence can be used as an effective 2D matching technique.