Active shape models—their training and application
Computer Vision and Image Understanding
The nature of statistical learning theory
The nature of statistical learning theory
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Support Vector Machines for Pattern Classification (Advances in Pattern Recognition)
Multi-Modal Human Verification Using Face and Speech
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Fusion of face and iris features for multimodal biometrics
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
On Improving the Efficiency of Eigenface Using a Novel Facial Feature Localization
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Touch me once and i know it's you!: implicit authentication based on touch screen patterns
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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This paper presents a multimodal biometric system based on face and hand images captured by a cell phone. The multimodal fusion is done at the feature extraction level. The nine facial models are built according to the number of features / points extracted from the face. Active shape models method is applied in order to find the concatenated string of facial points in the eyes, nose, and mouth areas. The face feature vector is constructed by applying Gabor filter to the image and extracting the key points found by an active shape model. The hand feature vector contains nine geometric measurements, including heights and widths of four fingers, and the width of the palm. Support vector machine is used as a classifier for a multimodal approach. One SVM machine is built for each person in the database to distinguish that person from the others. The database contains 113 individuals. As the experiments show, the best accuracy of up to 99.82% has been achieved for the model combining 8 eye, 12 mouth and 9 hand features.