The nature of statistical learning theory
The nature of statistical learning theory
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
A PDA-based Face Recognition System
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
Local Non-Negative Matrix Factorization as a Visual Representation
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Security Management for Mobile Devices by Face Recognition
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Very sparse random projections
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Cognitive Neuroscience
Deterministic neural classification
Neural Computation
EURASIP Journal on Advances in Signal Processing
A real-time, embedded face-annotation system
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Real-Time Face Verification for Mobile Platforms
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Semi-random subspace method for face recognition
Image and Vision Computing
A performance driven methodology for cancelable face templates generation
Pattern Recognition
A robust eye detection method in facial region
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
ICCSA'11 Proceedings of the 2011 international conference on Computational science and Its applications - Volume Part V
Incremental face recognition for large-scale social network services
Pattern Recognition
A complete and fully automated face verification system on mobile devices
Pattern Recognition
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Due to the increases in processing power and storage capacity of mobile devices over the years, an incorporation of realtime face recognition to mobile devices is no longer unattainable. However, the possibility of the realtime learning of a large number of samples within mobile devices must be established. In this paper, we attempt to establish this possibility by presenting a realtime training algorithm in mobile devices for face recognition related applications. This is differentiated from those traditional algorithms which focused on realtime classification. In order to solve the challenging realtime issue in mobile devices, we extract local face features using some local random bases and then a sequential neural network is trained incrementally with these features. We demonstrate the effectiveness of the proposed algorithm and the feasibility of its application in mobile devices through empirical experiments. Our results show that the proposed algorithm significantly outperforms several popular face recognition methods with a dramatic reduction in computational speed. Moreover, only the proposed method shows the ability to train additional samples incrementally in realtime without memory failure and accuracy degradation using a recent mobile phone model.