Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The NIST speaker recognition evaluation - overview methodology, systems, results, perspective
Speech Communication - Speaker recognition and its commercial and forensic applications
JPEG Still Image Data Compression Standard
JPEG Still Image Data Compression Standard
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: component-based versus global approaches
Computer Vision and Image Understanding - Special issue on Face recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Local Binary Patterns as an Image Preprocessing for Face Authentication
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Journal of Cognitive Neuroscience
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
A GMM parts based face representation for improved verification through relevance adaptation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face authentication with salient local features and static Bayesian network
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Hi-index | 0.00 |
In this paper we extend the Parts-Based approach of face verification by performing a frequency-based decomposition. The Parts-Based approach divides the face into a set of blocks which are then considered to be separate observations, this is a spatial decomposition of the face. This paper extends the Parts-Based approach by also dividing the face in the frequency domain and treating each frequency response from an observation separately. This can be expressed as forming a set of sub-images where each sub-image represents the response to a different frequency of, for instance, the Discrete Cosine Transform. Each of these sub-images is treated separately by a Gaussian Mixture Model (GMM) based classifier. The classifiers from each sub-image are then combined using weighted summation with the weights being derived using linear logistic regression. It is shown on the BANCA database that this method improves the performance of the system from an Average Half Total Error Rate of 24.38% to 15.17% when compared to a GMM Parts-Based approach on Protocol P.