  Facial image retrieval is a challenging task since faces have many similar
features (areas), which makes it difficult for the retrieval systems to
distinguish faces of different people. With the advent of deep learning, deep
networks are often applied to extract powerful features that are used in many
areas of computer vision. This paper investigates the application of different
deep learning models for face image retrieval, namely, Alexlayer6, Alexlayer7,
VGG16layer6, VGG16layer7, VGG19layer6, and VGG19layer7, with two types of
dictionary learning techniques, namely $K$-means and $K$-SVD. We also
investigate some coefficient learning techniques such as the Homotopy, Lasso,
Elastic Net and SSF and their effect on the face retrieval system. The
comparative results of the experiments conducted on three standard face image
datasets show that the best performers for face image retrieval are Alexlayer7
with $K$-means and SSF, Alexlayer6 with $K$-SVD and SSF, and Alexlayer6 with
$K$-means and SSF. The APR and ARR of these methods were further compared to
some of the state of the art methods based on local descriptors. The
experimental results show that deep learning outperforms most of those methods
and therefore can be recommended for use in practice of face image retrieval
