act-detect is evaluated on a face-detection task using a publicly available image set.
The case is evaluated on a number of factors.
The proposed method is evaluated on the face recognition problem under different challenges.
The framework is evaluated on the Georgia Tech face database [12], Notre Dame (ND) Twins face database [13], FRGC [14], and the MORPH Album 1 database [15].
In the first category, the proposed method is evaluated on the GT face database and LFW database.
Integrating the (22) in the dual cell (S_{i}) with the application of the Gauss-Green theorem yields frac{partial }{partial t} int_{S_{i}} u,mathrm{d}s + sum_{l = 1} ^{tau ( i ) } L_{i,l}F_{i,l} = 0, (23) where (F_{i,l}) is the numerical flux to be evaluated on the cell face between the dual cells (S_{i}) and (S_{rho ( i,l ) }).
Shaji and Chandran [15] also evaluated on the Mocap-Face dataset [9] with additive noise.
The method is evaluated on three widely used face recognition databases: AR, FERET and LFW, with the very popular LBP operator and some of its extensions.
On the trials that were not evaluated, a face with a neutral expression was presented.
The vulnerability of 8,217 COPD patients was evaluated using a face-to-face questionnaire to obtain information on general characteristics, awareness, beliefs, medication usage, acute exacerbation of the disease, and economic burdens.
This approach is evaluated on LFW dataset [48] which contains 13,233 face images with variations in pose, illumination, expression, in-plane rotation, resolution, and localization (resulting in scale or translation error).
