  Computation of singular value decomposition (SVD) has been a topic of concern
by many numerical linear algebra researchers. Fast SVD has been a very
effective tool in computer vision in a number of aspects, such as: face
recognition, eye tracking etc. At the present state of the art fast and
fixed-point power efficient SVD algorithm needs to be developed for real-time
embedded computing. The work in this paper is the genesis of an attempt to
build an on-board real-time face and eye tracking system for human drivers to
detect loss of attention due to drowsiness or fatigue. A major function of this
on-board system is quick customization. This is carried out when a new driver
comes in. The face and eye images are recorded while instructing the driver for
making specific poses. The eigen faces and eigen eyes are generated at several
resolution levels and stored in the on-board computer. The discriminating eigen
space of face and eyes are determined and stored in the on-board flash memory
for detection and tracking of face and eyes and classification of eyes (open or
closed) as well. Therefore, fast SVD of image covariance matrix at various
levels of resolution needs to be carried out to generate the eigen database. As
a preliminary step, we review the existing symmetric SVD algorithms and
evaluate their feasibility for such an application. In this article, we compare
the performance of (1) Jacobi's, (2) Hestenes', (3) Golub-Kahan, (4)
Tridiagonalization and Symmetric QR iteration and (5) Tridiagonalization and
Divide and Conquer algorithms. A case study has been demonstrated as an
example.
