  Astronomical databases currently provide high-volume spectroscopic and
photometric data. While spectroscopic data is better suited to the analysis of
many astronomical objects, photometric data is relatively easier to obtain due
to shorter telescope usage time. Therefore, there is a growing need to use
photometric information to automatically identify objects for further detailed
studies, specially H{\alpha} emission line stars such as Be stars. Photometric
color-color diagrams (CCDs) are commonly used to identify this kind of objects.
However, their identification in CCDs is further complicated by the reddening
effect caused by both the circumstellar and interstellar gas. This effect
prevents the generalization of candidate identification systems. Therefore, in
this work we evaluate the use of neural networks to identify Be star candidates
from a set of OB-type stars. The networks are trained using a labeled subset of
the VPHAS+ and 2MASS databases, with filters u, g, r, H{\alpha}, i, J, H, and
K. In order to avoid the reddening effect, we propose and evaluate the use of
reddening-free Q indices to enhance the generalization of the model to other
databases and objects. To test the validity of the approach, we manually
labeled a subset of the database, and use it to evaluate candidate
identification models. We also labeled an independent dataset for cross dataset
evaluation. We evaluate the recall of the models at a 99% precision level on
both test sets. Our results show that the proposed features provide a
significant improvement over the original filter magnitudes.
