Analysis of Human Intelligence in Identifying Persons Native through the Features of Facial Image

Hiremani, Vani A.; Senapati, Kishore Kumar

doi:10.1109/icait47043.2019.8987298

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…This section describes an empirical examination carried on humans to analyze and understand which features they considered to classify given faces to their regions. Let S be a human intelligence system consisting of input image I i (picked from I 2895 (face database)), questionnaire Q (set of 9 questions, i.e., Q = {q 1 ……… q 9 }), feature vector Fe (Fe = {f 1 , f 2 …….… f L }) [8] extracted by human identifiers, answer A (the subset of features Fe in terms of answers, A ⊆ Fe), and C the result of binary classification (w 1 and w 2 ). The representation of AHIS(S) is described as S = {I i | I i V I 2895 , Q, Fe, A, C}.…”

Section: Ahis Model: Classification Taskmentioning

confidence: 99%

“…Each identifier is given a set consisting of ± 19 images. Each identifier is interrogated with a set of fundamental questions [8] and images. The questions are framed to record how the identifier perceives an image, the gender, the discriminating features, and any additional factors those favored identifiers to guess the region.…”

Section: Human Interrogationmentioning

confidence: 99%

“…Augmentation of these symmetric features can bring tangible gain in classification. The experimental results of human visual analysis have shown an accuracy of 88% when both identifiers and persons in the image are from different regions [8]. 96% accuracy is achieved when both are from the same region [9].…”

Section: Introductionmentioning

confidence: 99%

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

Hiremani¹,

Senapati²

2023

Computer Systems Science and Engineering

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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.

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Section: Ahis Model: Classification Taskmentioning

confidence: 99%

Section: Human Interrogationmentioning

confidence: 99%