C.S7-fevt7 and K. K. Paliwal, "Likelihood normalization for face authentication in variable recording conditions," in Proc IEEE Intl. Conf. Image Proc4,74 ICIP, vol. 1, 2002, pp. 301--304. Home/Search Document Details and Download
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The VidTIMIT Database - Sanderson (2002) Self-citation (Sanderson) (Correct)
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C. Sanderson and K. K. Paliwal, "Likelihood Normalization for Face Authentication in Variable Recording Conditions", Proc. International Conf. Image Processing, Rochester, New York, 2002.
The VidTIMIT Database - Sanderson (2002) Self-citation (Sanderson) (Correct)
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C. Sanderson and K. K. Paliwal, "Likelihood Normalization for Face Authentication in Variable Recording Conditions", Proc. International Conf. Image Processing, Rochester, New York, 2002.
The VidTIMIT Database - Sanderson (2002) Self-citation (Sanderson) (Correct)
....fadg0, faks0, fcft0, fcmh0, mstk0, mtas1, mtmr0 and mwbt0 (i.e. 4 female and 4 male) are to be used only for impostor tests; this leaves 35 subjects for true claimant tests. In total, there are 1120 (35 8) impostor and 140 (35 4) true claimant tests. Publications using this protocol: [12, 13, 14]. 5.3 Proposed Protocol II: A Priori Performance Type A In this protocol, Session 1 of the database is used for training the client models, Session 2 is used to find the a priori decision threshold (or parameters for an equivalent decision mechanism) and Session 3 is used to find the final ....
C. Sanderson and K. K. Paliwal, "Likelihood Normalization for Face Authentication in Variable Recording Conditions", Proc. International Conf. Image Processing, Rochester, New York, 2002.
Automatic Person Verification Using Speech and Face Information - Sanderson (2002) (1 citation) Self-citation (Sanderson) (Correct)
....set is more robust to compression artefacts and white Gaussian noise. To keep consistency with traditional matrix notation, pixel locations (and image sizes) throughout this chapter are described using the row(s) first, followed by the column(s) Publications resulting from this research: [131, 132, 133, 134, 136, 137, 138]. In UBM alt normalization, both the client and the impostor models are generated using the EM algorithm, which is in contrast to UBM normalization where the client models are generated by adapting the impostor model. 5.2 Summary of Past Face Recognition Approaches This section presents a ....
C. Sanderson and K. K. Paliwal, "Likelihood Normalization for Face Authentication in Variable Recording Conditions", Proc. International Conf. Image Processing, Rochester, 2002, pp. 301-304 (Vol. 1).
The VidTIMIT Database - Sanderson (2002) Self-citation (Sanderson) (Correct)
....fadg0, faks0, fcft0, fcmh0, mstk0, mtas1, mtmr0 and mwbt0 (i.e. 4 female and 4 male) are to be used only for impostor tests; this leaves 35 subjects for true claimant tests. In total, there are 1120 (35 4 8) impostor and 140 (35 4) true claimant tests. Publications using this protocol: [12, 13, 14]. 5.3 Proposed Protocol II: A Priori Performance Type A In this protocol, Session 1 of the database is used for training the client models, Session 2 is used to nd the a priori decision threshold (or parameters for an equivalent decision mechanism) and Session 3 is used to nd the nal ....
C. Sanderson and K. K. Paliwal, \Likelihood Normalization for Face Authentication in Variable Recording Conditions", Proc. International Conf. Image Processing, Rochester, New York, 2002.
Automatic Person Verification Using Speech and Face Information - Sanderson (2002) (1 citation) Self-citation (Sanderson) (Correct)
....of the contribution of each block. Results show that depending on the window more robust to compression artefacts and white Gaussian noise. throughout this chapter are described using the row(s) first, followed by the column(s) Publications resulting from this research: 127] 129] [130], 131] 132] 133] and [134] In UBM alt normalization, both the client and the impostor models are generated using the EM algorithm, which is in contrast to UBM normalization where the client models are generated by adapting the impostor model. 5.2 Summary of Past Face Recognition ....
C. Sanderson and K. K. Paliwal, "Likelihood Normalization for Face Authentication in Variable Recording Conditions", Proc. International Conf. Image Processing, Rochester, New York, 2002.
CTIONS ONS7et-yMAN, - Andcyberneticsfi Art Applications (2004) (Correct)
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C.S7-fevt7 and K. K. Paliwal, "Likelihood normalization for face authentication in variable recording conditions," in Proc IEEE Intl. Conf. Image Proc4,74 ICIP, vol. 1, 2002, pp. 301--304.
Target Dependent - Score Normalization Techniques (Correct)
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Sanderson, C., Paliwal, K.K.: Likelihood normalization for face authentication in variable recording conditions. In: Proc. of ICIP. Volume 1. (2002) 301--304 500
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