IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expert Conciliation for Multi Modal Person Authentication Systems by Bayesian Statistics
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Face Recognition Vendor Test 2002
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Evaluation of Multimodal 2D+3D Face Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Score normalization in multimodal biometric systems
Pattern Recognition
The BANCA database and evaluation protocol
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
A classification approach to multi-biometric score fusion
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Personal recognition using hand shape and texture
IEEE Transactions on Image Processing
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
An effective multi-biometrics solution for embedded device
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A new multi-purpose audio-visual UNMC-VIER database with multiple variabilities
Pattern Recognition Letters
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A biometric sample collected in an uncontrolled outdoor environment varies significantly from its indoor version. Sample variations due to outdoor environmental conditions degrade the performance of biometric systems that otherwise perform well with indoor samples. In this study, we quantitatively evaluate such performance degradation in the case of a face and a voice biometric system. We also investigate how elementary combination schemes involving min-max or z normalization followed by the sum or max fusion rule can improve performance of the multi-biometric system. We use commercial biometric systems to collect face and voice samples from the same subjects in an environment that closely mimics the operational scenario. This realistic evaluation on a dataset of 116 subjects shows that the system performance degrades in outdoor scenarios but by multi-modal score fusion the performance is enhanced by 20%. We also find that max rule fusion performs better than sum rule fusion on this dataset. More interestingly, we see that by using multiple samples of the same biometric modality, the performance of a unimodal system can approach that of a multi-modal system.