  We present a novel unsupervised learning approach to image landmark discovery
by incorporating the inter-subject landmark consistencies on facial images.
This is achieved via an inter-subject mapping module that transforms original
subject landmarks based on an auxiliary subject-related structure. To recover
from the transformed images back to the original subject, the landmark detector
is forced to learn spatial locations that contain the consistent semantic
meanings both for the paired intra-subject images and between the paired
inter-subject images. Our proposed method is extensively evaluated on two
public facial image datasets (MAFL, AFLW) with various settings. Experimental
results indicate that our method can extract the consistent landmarks for both
datasets and achieve better performances compared to the previous
state-of-the-art methods quantitatively and qualitatively.
