%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{sanders-mmua03,
         author = {Sanderson, Conrad and Bengio, Samy},
       projects = {Idiap},
          month = {12},
          title = {Augmenting {F}rontal {F}ace {M}odels for Non-{F}rontal {V}erification},
      booktitle = {{P}roceedings of the 2003 {W}orkshop on {M}ultimodal User Authentication ({MMUA}'03)},
           year = {2003},
        address = {Santa Barbara, California},
       crossref = {sanders-rr-03-60},
ipdmembership={learning},
}



crossreferenced publications: 
@TECHREPORT{sanders-rr-03-60,
         author = {Sanderson, Conrad and Bengio, Samy},
       projects = {Idiap},
          month = {11},
          title = {{F}ace {V}erification Using {S}ynthesized Non-{F}rontal {M}odels},
           type = {Idiap-RR},
         number = {Idiap-RR-60-2003},
           year = {2003},
    institution = {IDIAP},
       abstract = {{NOTE}: {THIS} {REPORT} {HAS} {BEEN} {SUPERSEDED} {BY} {IDIAP-RR} 04-04. {I}n this report we address the problem of non-frontal face verification when only a frontal training image is available (e.g. a passport photograph) by augmenting a client's frontal face model with artificially synthesized models for non-frontal views. In the framework of a {G}aussian {M}ixture {M}odel ({GMM}) based classifier, two techniques are proposed for the synthesis: {UBM}diff and {L}in{R}eg. {B}oth techniques rely on prior information and learn how face models for the frontal view are related to face models at a non-frontal view. {T}he synthesis and augmentation approach is evaluated by applying it to two face verification systems: {P}rincipal {C}omponent Analysis ({PCA}) based and {DCT}mod2 based; the two systems are a representation of holistic and non-holistic approaches, respectively. {R}esults from experiments on the {FERET} database suggest that in almost all cases, frontal model augmentation has beneficial effects for both systems; they also suggest that the {L}in{R}eg technique (which is based on multivariate regression of classifier parameters) is more suited to the {PCA} based system and that the {UBM}diff technique (which is based on differences between two general face models) is more suited to the {DCT}mod2 based system. The results also support the view that the standard {DCT}mod2/{GMM} system (trained on frontal faces) is less affected by out-of-plane rotations than the corresponding {PCA}/{GMM} system; moreover, the {DCT}mod2/{GMM} system using augmented models is, in almost all cases, more robust than the corresponding {PCA}/{GMM} system.},
            pdf = {https://publications.idiap.ch/attachments/reports/2003/rr03-60.pdf},
     postscript = {ftp://ftp.idiap.ch/pub/reports/2003/rr03-60.ps.gz},
ipdmembership={learning},
}