Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination Cones for Recognition under Variable Lighting: Faces
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Face Recognition Using Binary Image Metrics
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
An Affine Coordinate Based Algorithm for Reprojecting the Human Face for Identification Tasks
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
Color Illumination Models for Image Matching and Indexing
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Face Recognition From One Example View
Face Recognition From One Example View
Robust face imagematching under illumination variations
EURASIP Journal on Applied Signal Processing
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Face matching is an essential step for face recognition and face verification. It is difficult to achieve robust face matching under various image acquisition conditions. In this paper, an illumination-insensitive face image-matching algorithm is proposed. This algorithm is based on an accumulated consistency measure of corresponding normalized gradients at face contour locations between two comparing face images under different lighting conditions. To solve the matching problem due to lighting changes between two face images, we first use a consistency measure, which is defined by the inner product between two normalized gradient vectors at the corresponding locations in the two images. Then we compute the sum of the individual consistency measures of the normalized gradients at all the contour pixels to be the robust matching measure between two face images. To better compensate for lighting variations, three face images with very different lighting directions for each person are used for robust face image matching. The Yale Face Database, which contains images acquired under three different lighting conditions for each person, are used to test the proposed algorithm. The experimental results show good recognition results under different lighting conditions by using the proposed illumination-insensitive face matching algorithm.