  Malnutrition is a global health crisis and is the leading cause of death
among children under five. Detecting malnutrition requires anthropometric
measurements of weight, height, and middle-upper arm circumference. However,
measuring them accurately is a challenge, especially in the global south, due
to limited resources. In this work, we propose a CNN-based approach to estimate
the height of standing children under five years from depth images collected
using a smart-phone. According to the SMART Methodology Manual [5], the
acceptable accuracy for height is less than 1.4 cm. On training our deep
learning model on 87131 depth images, our model achieved an average mean
absolute error of 1.64% on 57064 test images. For 70.3% test images, we
estimated height accurately within the acceptable 1.4 cm range. Thus, our
proposed solution can accurately detect stunting (low height-for-age) in
standing children below five years of age.
