Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD

TL;DR

This paper introduces a computationally efficient multi-class face recognition method combining ASURF features, VLAD feature quantization, and Cloud Forest classification, achieving improved accuracy on benchmark datasets.

Contribution

It presents a novel face recognition pipeline integrating ASURF, VLAD, and Cloud Forest, enhancing recognition accuracy and efficiency over existing ensemble classifiers.

Findings

Achieved 2-12% accuracy improvement over other ensemble methods.

Demonstrated effectiveness on FACES95, FACES96, and ORL datasets.

Reduced computational time for face recognition tasks.

Abstract

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.