  The paper posits a computationally-efficient algorithm for multi-class facial
image classification in which images are constrained with translation,
rotation, scale, color, illumination and affine distortion. The proposed method
is divided into five main building blocks including Haar-Cascade for face
detection, Bilateral Filter for image preprocessing to remove unwanted noise,
Affine Speeded-Up Robust Features (ASURF) for keypoint detection and
description, Vector of Locally Aggregated Descriptors (VLAD) for feature
quantization and Cloud Forest for image classification. The proposed method
aims at improving the accuracy and the time taken for face recognition systems.
The usage of the Cloud Forest algorithm as a classifier on three benchmark
datasets, namely the FACES95, FACES96 and ORL facial datasets, showed promising
results. The proposed methodology using Cloud Forest algorithm successfully
improves the recognition model by 2-12\% when differentiated against other
ensemble techniques like the Random Forest classifier depending upon the
dataset used.
