Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Fast features for face authentication under illumination direction changes
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A comparative study of automatic face verification algorithms on the BANCA database
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
The BANCA database and evaluation protocol
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Comparison of MLP and GMM classifiers for face verification on XM2VTS
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
IEEE Transactions on Pattern Analysis and Machine Intelligence
On transforming statistical models for non-frontal face verification
Pattern Recognition
Face authentication using adapted local binary pattern histograms
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Information Sciences: an International Journal
Towards computer understanding of human interactions
MLMI'04 Proceedings of the First international conference on Machine Learning for Multimodal Interaction
Automatic person annotation of family photo album
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
Audio-Visual processing in meetings: seven questions and current AMI answers
MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction
Video-Based face verification with local binary patterns and SVM using GMM supervectors
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
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It has been shown previously that systems based on local features and relatively complex generative models, namely 1D Hidden Markov Models (HMMs) and pseudo-2D HMMs, are suitable for face recognition (here we mean both identification and verification). Recently a simpler generative model, namely the Gaussian Mixture Model (GMM), was also shown to perform well. In this paper we first propose to increase the performance of the GMM approach (without sacrificing its simplicity) through the use of local features with embedded positional information; we show that the performance obtained is comparable to 1D HMMs. Secondly, we evaluate different training techniques for both GMM and HMM based systems. We show that the traditionally used Maximum Likelihood (ML) training approach has problems estimating robust model parameters when there is only a few training images available; we propose to tackle this problem through the use of Maximum a Posteriori (MAP) training, where the lack of data problem can be effectively circumvented; we show that models estimated with MAP are significantly more robust and are able to generalize to adverse conditions present in the BANCA database.