Multi-modal classification plays a vital role for the real-time applications since most of the conventional models are independent of homogeneous features with single classifier. In addition, traditional directional gradient descriptors are difficult to find the heterogeneous features on different biometric classification. Feature extraction, segmentation, and multi-modal classification are the essential key factors that improve the true positive rate, error rate, and false positive rate of human-based recognition systems. In this work, a hybrid ensemble-based feature selection ranking measure, hybrid segmentation, and ensemble multi-class multi-modal classification framework on different biometric features is designed. Experimental results show that the proposed multi-class multi-modal ensemble classification framework has better optimization in terms of false positive rate, error rate, and precision than the conventional homogeneous local gradient feature extraction-based classification models on different features.
Chopparapu et al. (Wed,) studied this question.