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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Chickaramanna, Suguna Guttoor
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Computer Science & IT Engineering

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Identification and classification of prakriti of human using facial features Chickaramanna, Suguna Guttoor; Thippeswamy, Veerabhadrappa Sondekere
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2093-2101

Abstract

Changes in the lifestyle of an individual, has lead to several diseases that have emerged due to the imbalance of doshas components. Ayurveda practitioners could identify the imbalance of dosha and relate the root cause of imbalance of doshas. Analysis of dosha varies from practitioner to practitioner and it requires well practiced practitioner to identify dosha. To overcome, darshana method was adopted to implement automatic identification of predominant dosha using facial features such as face, eyes, nose, mouth, and skin color. Computer vision and image processing techniques were made attempt in Ayurveda domain, for identification of predominate prakriti, age, and gender of the subject. Eye aspect ratio (EAR), nose aspect ratio (NAR), mouth aspect ratio (MAR), and skin color was computed based on Euclidean distance to identify on-live predomaint prakriti of an individual. The values of MAR ≤ 0.5, EAR ≤ 0.1, NAR ≤ 0.8 as identified as vata; 0.5 ≥ MAR ≤ 0.6, 0.1 ≥ EAR ≤ 0.2, 0.8 ≥ NAR ≤ 1 as identified as pitta; and MAR ≥ 0.6, EAR ≥ 0.2 and NAR ≥ 1 as identified as kapha dosha. With the features MAR, EAR, and NAR classification of predominant prakriti was carried out with an accuracy of 87.5% with support vector classifier.
Co-Authors Thippeswamy, Veerabhadrappa Sondekere