Large-scale orthogonal integer wavelet transform features-based active support vector machine for multi-class face recognition

Support vector machines are widely utilised in the field of Face Recognition (FR) but it suffers from the drawback of high-computational time. In proposed work, new active set strategy is utilised for support vector machines on Integer Wavelet Transform (IWT) based large scale facial features with low-computational time. Lifting scheme-based significant localised wavelet features are extracted using IWT based on orthogonal wavelets. Large Scale Orthogonal-IWT (LSOI) features with maximum covariance are then projected into eigen space from where robust training and testing features are selected. For classification of data, Active Support Vector Machine (ASVM) based machine learning technique is utilised which generates a less complex procedure compared to traditional support vector machine. ASVM aims to solve a fixed number of linear equations for One-vs-One (OVO) and One-vs-All (OVA) multiclass FR. Extensive experiments on Yale, ORL, AR, JAFFE and Georgia-Tech databases have revealed high performance compared to existing FR techniques.