Optimal Weighted Logarithmic Transformation Converted HMOG Features for Automatic Smart Phone Authentication

This paper intends to develop an automatic behavior-based smart phone authentication model by three major phases: feature extraction, weighted logarithmic transformation, and classification. Initially, from the data related to the touches/gesture of the smartphone user, hand movement, orientation, and grasp (HMOG), features are extracted with the aid of grasp resistance and grasp stability. These extracted features are mapped within the particular range by normalizing HMOG. These normalized data are multiplied with the weights followed by logarithmic transformation in the weighted logarithmic transformation phase. As a novelty, the decision-making process related to the logarithmic and weight selection is based on the improved optimization algorithm, called modified threshold-based whale optimization algorithm (MT-WOA). The final feature vectors are fed to DBN for recognizing the authorized users. Finally, a performance-based evaluation is performed between the MT-WOA+DBN and the existing models in terms of various relevant performance measures.