  Cascaded regression has been recently applied to reconstructing 3D faces from
single 2D images directly in shape space, and achieved state-of-the-art
performance. This paper investigates thoroughly such cascaded regression based
3D face reconstruction approaches from four perspectives that are not well
studied yet: (i) The impact of the number of 2D landmarks; (ii) the impact of
the number of 3D vertices; (iii) the way of using standalone automated landmark
detection methods; and (iv) the convergence property. To answer these
questions, a simplified cascaded regression based 3D face reconstruction method
is devised, which can be integrated with standalone automated landmark
detection methods and reconstruct 3D face shapes that have the same pose and
expression as the input face images, rather than normalized pose and
expression. Moreover, an effective training method is proposed by disturbing
the automatically detected landmarks. Comprehensive evaluation experiments have
been done with comparison to other 3D face reconstruction methods. The results
not only deepen the understanding of cascaded regression based 3D face
reconstruction approaches, but also prove the effectiveness of proposed method.
