Human and Machine Vision Based Indian Race Classification Using Modified-Convolutional Neural Network

Abstract

The inter-class face classification problem is more reasonable than the intra-class classification problem. To address this issue, we have carried out empirical research on classifying Indian people to their geographical regions. This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India, referring to human vision. We have created an Automated Human Intelligence System (AHIS) to evaluate human visual capabilities. Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features. We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy. The proposed model achieved mean F1 and Matthew Correlation Coefficient (MCC) of 0.92 and 0.84, respectively, on the validation set, outperforming the traditional Convolutional Neural Network (CNN). The CNN-Contoured Face (CNN-FC) model is developed to train contoured face images to investigate the influence of face shape. Finally, to cross-validate the accuracy of these models, the traditional CNN model is trained on the same dataset. With an accuracy of 92.98%, the Modified-CNN (M-CNN) model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems. A novel Indian regional face dataset is created for supporting this supervised classification work, and it will be available to the research community.

Spatially Structured Environmental Analysis of Marine Ecological Landscapes Based on Machine Vision

“…In the process of marine exploitation, if the limit of its recovery capacity is not exceeded, the marine ecosystem will recover to a certain extent through the cyclic evolution of marine organisms and the resource environment. However, if the recovery capacity limit is exceeded, the marine ecosystem will not only fail to recover, but will also develop in a reverse degradation manner, leading to the collapse of the marine ecosystem [22]. The improvement of marine productivity should not be carried out blindly and uncontrollably; otherwise, the marine ecosystem will eventually be extinguished if the recovery capacity of the marine ecosystem is exceeded.…”