Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Authentication by Fusing Face and Speech Information
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Communications of the ACM - Multimodal interfaces that flex, adapt, and persist
On the Use of SIFT Features for Face Authentication
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Feature Fusion of Face and Gait for Human Recognition at a Distance in Video
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Combining face and iris biometrics for identity verification
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Combining left and right irises for personal authentication
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Biometric authentication system using reduced joint feature vector of iris and face
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fusion of near infrared face and Iris biometrics
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Genetic programming for multibiometrics
Expert Systems with Applications: An International Journal
Multibiometric system using level set method and particle swarm optimization
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Multibiometric system using distance regularized level set method and particle swarm optimization
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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Multi-biometrics has recently emerged as a mean of more robust and efficient personal verification and identification. Exploiting information from multiple sources at various levels i.e., feature, score, rank or decision, the false acceptance and rejection rates can be considerably reduced. Among all, feature level fusion is relatively an understudied problem. This paper addresses the feature level fusion of multi-modal and multi-unit sources of information. For multi-modal fusion the face and iris biometric traits are considered, while the multi-unit fusion is applied to merge the data from the left and right iris images. The proposed approach computes the SIFT features from both biometric sources, either multi-modal or multi-unit. For each source, feature selection on the extracted SIFT features is performed via spatial sampling. Then these selected features are finally concatenated together into a single feature super-vector using serial fusion. This concatenated super feature vector is used to perform classification. Experimental results from face and iris standard biometric databases are presented. The reported results clearly show the performance improvements in classification obtained by applying feature level fusion for both multi-modal and multi-unit biometrics in comparison to uni-modal classification and score level fusion.