Multimodal human eye blink recognition method using feature level fusion for exigency detection

In this paper, a precise multimodal eye blink recognition method using feature level fusion (M_mERM_FLF) is proposed. A new feature: eye-eyebrow facet ratio (EE_BFR) (formed by fusing eye facet ratio (EFR: ratio of diagonal length and width of eye) and eyebrow to nose facet ratio (E_BNFR: distance between eyebrow landmarks and nose landmark)) for approximating the eye state is computed. Initially, an improved intellectual framework (Sagacious Information Recuperation Technique) that senses the emergency state using information retrieved from eye blinks, pulse rate as well as behavioral patterns(emotions) exhibited by an individual is presented. Further a novel multimodal method (M_mERM_FLF) for detection and counting of eye blinks is implemented. For training, one state-of-the-art database—ZJU is used. To additionally improve the performance, feature-level fusion schemes [simple concatenate and fusion codes (gaborization)] are enforced and equated. Receiver operating characteristics, error rate, sensitivity, specificity, and precision are used to demonstrate the performance of the proposed method qualitatively and quantitatively. Accuracy with proposed M_mERM_FLF is increased to 99.02% (using EE_BGFR method with bagged ensemble classifier) in comparison to unimodal eye blink recognition system (97.60%). 99.80% genuine blinks are classified by M_mERM_FLF (when gaborization fusion is used) using simple tree classifier.