Unconstrained Face Recognition using ASURF and Cloud-Forest Classifier optimized with VLAD
This work addresses face recognition under various distortions, offering incremental improvements for computer vision applications.
The paper tackled unconstrained face recognition by proposing a method combining ASURF, VLAD, and Cloud Forest classifier, achieving a 2-12% improvement in accuracy over Random Forest on benchmark datasets.
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.