Computes the ISV session offset
Algorithms have at least one input and one output. All algorithm endpoints are organized in groups. Groups are used by the platform to indicate which inputs and outputs are synchronized together. The first group is automatically synchronized with the channel defined by the block in which the algorithm is deployed.
| Endpoint Name | Data Format | Nature |
|---|---|---|
| statistics | tutorial/gmm_statistics/1 | Input |
| isv_offset | system/array_1d_floats/1 | Output |
| Endpoint Name | Data Format | Nature |
|---|---|---|
| ubm | tutorial/gmm/1 | Input |
| isvbase | tutorial/isvbase/1 | Input |
xxxxxxxxxximport bobimport numpydef gmm_from_data(data): """Unmangles a bob.machine.GMMMachine from a BEAT Data object""" dim_c, dim_d = data.means.shape gmm = bob.machine.GMMMachine(dim_c, dim_d) gmm.weights = data.weights gmm.means = data.means gmm.variances = data.variances gmm.variance_thresholds = data.variance_thresholds return gmmdef isvbase_from_data(data, ubm): """Unmangles a bob.machine.ISVBase from a BEAT Data object""" dim_cd, dim_u = data.subspace_u.shape isvbase = bob.machine.ISVBase(ubm, dim_u) isvbase.u = data.subspace_u isvbase.d = data.subspace_d return isvbasedef stats_from_data(data): """Unmangles a bob.machine.GMMStats from a BEAT Data object""" dim_c, dim_d = data.sum_px.shape stat = bob.machine.GMMStats(dim_c, dim_d) stat.t = long(data.t) stat.n = data.n stat.sum_px = data.sum_px stat.sum_pxx = data.sum_pxx return statclass Algorithm: def __init__(self): self.ubm = None self.isvbase = None def process(self, inputs, outputs): # retrieve the UBM once if self.ubm is None: inputs['ubm'].next() self.ubm = gmm_from_data(inputs['ubm'].data) # retrieve the ISVBase once if self.isvbase is None: inputs['isvbase'].next() self.isvbase = isvbase_from_data(inputs['isvbase'].data, self.ubm) stats = stats_from_data(inputs["statistics"].data) projected_isv = numpy.ndarray(shape=(self.ubm.dim_c*self.ubm.dim_d,), dtype=numpy.float64) model = bob.machine.ISVMachine(self.isvbase) model.estimate_ux(stats, projected_isv) # outputs data outputs["isv_offset"].write({ 'value': projected_isv, }) return TrueThe code for this algorithm in Python
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Given a feature vector, a GMM and a U subspace, computes the session offset (xi, j).
Specific details can be found in [McCool2013].
This algorithm relies on the Bob library.
The inputs are:
The output, isv_offset, is the latent variable xi, j ( Eq. (29) in [McCool2013]) that corresponds to the session offset.
| [McCool2013] | (1, 2) McCool, Christopher, et al. "Session variability modelling for face authentication." IET biometrics 2.3 (2013): 117-129. |
| Updated | Name | Databases/Protocols | Analyzers | |||
|---|---|---|---|---|---|---|
| smarcel/tutorial/full_isv/2/mobio_male-gmm_100Gx10I-isv_50Ux10Ix4R-dct_12Bx8Ox45C-seed101 | mobio/1@male | tutorial/eerhter_postperf_iso/1 | ||||
| tutorial/tutorial/full_isv/2/bancaMc_isv_DCT12x8_100G_U50 | banca/1@Mc | tutorial/eerhter_postperf_iso/1 | ||||
| tutorial/tutorial/full_isv/2/xm2vtsLp1_isv_DCT12x8_100G_U50 | xm2vts/1@lp1 | tutorial/eerhter_postperf_iso/1 | ||||
| tutorial/tutorial/full_isv/2/mobioMale_isv_DCT12x8_100G_U50 | mobio/1@male | tutorial/eerhter_postperf_iso/1 | ||||
| tutorial/tutorial/full_isv/2/bancaP_isv_DCT12x8_100G_U50 | banca/1@P | tutorial/eerhter_postperf_iso/1 | ||||
| tutorial/tutorial/full_isv/2/atnt_isv_DCT12x8_100G_U50 | atnt/1@idiap_test_eyepos | tutorial/eerhter_postperf_iso/1 |
This table shows the number of times this algorithm has been successfully run using the given environment. Note this does not provide sufficient information to evaluate if the algorithm will run when submitted to different conditions.