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arXiv · 2102.11361

FaCells. Teaching Machines the Language of Lines: Per Point Attribute Scores for Face-Sketch Classification

Xavier Ignacio Gonzalez
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Abstract

FaCells is a method, and an exhibition, that turns model internals into line based artworks. Aligned face photographs (CelebA, 260k images, 40 attributes) are translated into vector sketches suitable for an XY plotter. We study how to 'write' these drawings for a sequence model, comparing absolute vs. relative point encodings and random vs. travel-minimizing stroke order. A bidirectional LSTM is trained for attribute prediction; a minimal architectural change, removing the global average over the sequence and applying a Dense layer at each point, yields per point attribute scores. Aggregating points whose score exceeds an attribute specific threshold across many portraits produces new drawings we call FaCells: statistical abstractions of attributes such as Eyeglasses, Wavy Hair, or Bangs. Across ablations, absolute coordinates with travel-minimizing order and a global average readout perform best; this configuration is then adapted to produce per-point scores. Multilabel training over 40 attributes is stable, and attributes reaching at least 50% balanced accuracy are visualized as FaCells. Complementary notions (e.g., No_Beard) are constructed by selecting points below a negative threshold. FaCells foregrounds interpretability as a creative tool: the resulting works are plotter ready, reproducible, and inexpensive to realize, yet materially present. Presented at Spectrum Miami 2025, the project bridges data, model, and paper while acknowledging the limits of the labels and the biases of the dataset.

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Xavier Ignacio Gonzalez. 2025-11-20. FaCells. Teaching Machines the Language of Lines: Per Point Attribute Scores for Face-Sketch Classification. https://arxiv.org/abs/2102.11361

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