  Lines are human mental abstractions. A bunch of lines may form a drawing. A
set of drawings can feed an LSTM network input layer, considering each draw as
a list of lines and a line a list of points. This paper proposes the pointless
motive to classify the gender of celebrities' portraits as an excuse for
exploration in a broad, more artistic sense. Investigation results drove
compelling ideas here discussed. The experiments compared different ways to
represent draws to be input in a network and showed that an absolute format of
coordinates (x, y) was a better performer than a relative one (Dx, Dy) with
respect to prior points, most frequent in the reviewed literature. Experiments
also showed that, due to the recurrent nature of LSTMs, the order of lines
forming a drawing is a relevant factor for input in an LSTM classifier not
studied before. A minimum 'pencil' traveled length criteria for line ordering
proved suitable, possible by reducing it to a TSP particular instance. The best
configuration for gender classification appears with an LSTM layer that returns
the hidden state value for each input point step, followed by a global average
layer along the sequence, before the output dense layer. That result guided the
idea of removing the average in the network pipeline and return a per-point
attribute score just by adjusting tensors dimensions. With this trick, the
model detects an attribute in a drawing and also recognizes the points linked
to it. Moreover, by overlapping filtered lines of portraits, an attribute's
visual essence is depicted. Meet the FaCells.
