Introduction to artificial neural systems
Introduction to artificial neural systems
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face verification from 3D and grey level clues
Pattern Recognition Letters
Face recognition with one training image per person
Pattern Recognition Letters
Use of depth and colour eigenfaces for face recognition
Pattern Recognition Letters
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face Recognition Using Range Images
VSMM '97 Proceedings of the 1997 International Conference on Virtual Systems and MultiMedia
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Modeling and Recognition in 3-D
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distance measures for PCA-based face recognition
Pattern Recognition Letters
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
A procedure for face detection & recognition
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
A procedure for face detection & recognition
MS '07 The 18th IASTED International Conference on Modelling and Simulation
Computer Vision and Image Understanding
Rapid stereo-vision enhanced face detection
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
System for independent living --- new opportunity for visually impaired
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Hi-index | 12.05 |
Face recognition has been a popular research topic in computer vision. Numerous different face recognition techniques have been developed owing to the growing number of real world applications. This study presents a face recognition method based on the stereo matching and eigenface techniques under a stereovision system. The proposed method aims to improve the performance of the 2D eigenface system by adding 3D information. 3D information (called disparity face) was derived by taking two faces of a subject simultaneously in different positions, and then the two faces were further matched using a scanlined-based asynchronous Hopfield neural network. After deriving 2D and 3D faces, we applied principal component analysis (PCA) to both faces to extract effective features for recognition. An experiment was conducted acquiring the facial images of 100 individuals. Each subject provides three pairs of faces with different expressions for training and testing. At the finals, the performance of face recognition using 2D faces, disparity faces, and a combination of the two was evaluated and compared. The experimental results reveal that a 3-5% improvement in recognition rate is achieved by using the additional depth information.