Abstract:
The performance of most face recognition systems (FRSs) in unconstrained environments
is widely noted to be sub-optimal. One reason for this poor performance may be the lack of highly
effective image pre-processing approaches, which are typically required before the feature extraction
and classi cation stages. Furthermore, it is noted that only minimal face recognition issues are typically
considered in most FRSs, thus limiting the wide applicability of most FRSs in real-life scenarios. Therefore,
it is envisaged that installing more effective pre-processing techniques, in addition to selecting the right
features for classi cation, will signi cantly improve the performance of FRSs. Hence, in this paper,
we propose an FRS, which comprises an effective image enhancement technique for face image preprocessing,
alongside a new set of hybrid features. Our image enhancement technique adopts the use of
a metaheuristic optimization algorithm for effective face image enhancement, irrespective of the conditions
in the unconstrained environment. This results in adding more features to the face image so that there
is an increase in recognition performance as compared with the original image. The new hybrid feature
is introduced in our FRS to improve the classi cation performance of the state-of-the-art convolutional
neural network architectures. Experiments on standard face databases have been carried out to con rm the
improvement in the performance of the face recognition system that considers all the constraints in the face
database.
