  We built the largest database for kinship recognition. The data were labeled
using a novel clustering algorithm that used label proposals as side
information to guide more accurate clusters. Great savings in time and human
input was had. Statistically, FIW shows enormous gains over its predecessors.
We have several benchmarks in kinship verification, family classification,
tri-subject verification, and large-scale search and retrieval. We also trained
CNNs on FIW and deployed the model on the renowned KinWild I and II to gain
SOTA. Most recently, we further augmented FIW with MM. Now, video dynamics,
audio, and text captions can be used in the decision making of kinship
recognition systems. We expect FIW will significantly impact research and
reality. Additionally, we tackled the classic problem of facial landmark
localization. A majority of these networks have objectives based on L1 or L2
norms, which inherit several disadvantages. The locations of landmarks are
determined from generated heatmaps from which predicted landmark locations get
penalized without accounting for the spread: a high scatter corresponds to low
confidence and vice-versa. To address this, we introduced an objective that
penalizes for low confidence. Another issue is a dependency on labeled data,
which is expensive to collect and susceptible to error. We addressed both
issues by proposing an adversarial training framework that leverages unlabeled
data to improve model performance. Our method claims SOTA on renowned
benchmarks. Furthermore, our model is robust with a reduced size: 1/8 the
number of channels is comparable to SOTA in real-time on a CPU. Finally, we
built BFW to serve as a proxy to measure bias across ethnicity and gender
subgroups, allowing us to characterize FR performances per subgroup. We show
performances are non-optimal when a single threshold is used to determine
whether sample pairs are genuine.
