AYURVEDIC DOSHAS IDENTIFICATION USING FACE AND BODY IMAGE FEATURES

Sanyam Jain, Taruna Chawla

Abstract


Ayurveda is an alternative medicine system with historical roots in India. In Ayurveda, any disease is considered to be caused by the imbalance of various Doshas (Vata, Pitta & Kapha) in human body. In order to diagnose this imbalance of doshas, there are several methods, out of which one is to observe visually the different features of the human body. Body type, skin type, hair type, eyes type, face type, etc possess different characteristics under the influence of various doshas. Hence the idea is to automate this process where we scan a body image using machine learning and deep learning algorithms and classify the different features into different doshas to finally conclude the dominant dosha as result.

Keywords


, ayurvedic doshas, dosha identification, ayurvedic diagnosis, vata pitta kapha, machine learning

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References


Art of Living Faculty, “Ayurveda body types,” Artoflivingretreatcenter.org, 12-Jun-2020. [Online]. Available: https://artoflivingretreatcenter.org/blog/know-yourself-by-knowing-your-ayurvedic-body-type/. [Accessed: 12-Jun-2021]

M. Kshirsagar and A. C. Magno, Ayurveda: A Quick Reference Handbook. Lotus Press, 2011.

V. Lad and U. Lad, “Determining Your Constitution,” in Ayurvedic Cooking for Self Healing, p. 1.

“Ayurvedic Examination,” Healthmantra.com. [Online]. Available: http://www.healthmantra.com/ayur/ayur-examination.shtml. [Accessed: 12-Jun-2021].

“Face Shape Classification Based on Region Similarity, Correlation and Fractal Dimensions,” Int. J. Comput. Sci. Issues, vol. 13, no. 1, pp. 24–31, Feb. 2016.

M. H. Mahoor and M. Abdel-Mottaleb, “Facial features extraction in color images using enhanced active shape model,” in 7th International Conference on Automatic Face and Gesture Recognition (FGR06), 2006, pp. 5 pp. – 148.

E. Saber and A. M. Tekalp, “Frontal-view face detection and facial feature extraction using color, shape and symmetry based cost functions,” Pattern Recognit. Lett., vol. 19, no. 8, pp. 669–680, Jun. 1998.

M. Yang, K. Kpalma, and J. Ronsin, “A Survey of Shape Feature Extraction Techniques,” in Pattern Recognition, P.-Y. Yin, Ed. IN-TECH, 2008, pp. 43–90.

H. Moon, R. Chellappa, and A. Rosenfeld, “Optimal edge-based shape detection,” IEEE Trans. Image Process., vol. 11, no. 11, pp. 1209–1227, Nov. 2002.

F. S. Cottle, “Statistical human body form classification: Methodology development and application,” Auburn.edu. [Online]. Available: https://etd.auburn.edu/bitstream/handle/10415/3071/Cottle%20Dissertation%202012.PDF?sequence=2. [Accessed: 12-Jun-2021].

S. L. Phung, A. Bouzerdoum, and D. Chai, “Skin segmentation using colour pixel classification: analysis and comparison,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 27, no. 1, pp. 148–154, Jan. 2005.

M. Osman, M. Maarof, and M. Rohani, “Towards Integrating Statistical Color Features for Human Skin Detection,” 2016.

F. Song, X. Tan, S. Chen, and Z.-H. Zhou, “A literature survey on robust and efficient eye localization in real-life scenarios,” Pattern Recognit., vol. 46, no. 12, pp. 3157–3173, Dec. 2013.

D. Borza, A. S. Darabant, and R. Danescu, “Real-Time Detection and Measurement of Eye Features from Color Images,” Sensors, vol. 16, no. 7, Jul. 2016.

L. Zhao, Z. Wang, G. Zhang, Y. Qi, and X. Wang, “Eye state recognition based on deep integrated neural network and transfer learning,” Multimed. Tools Appl., vol. 77, no. 15, pp. 19415–19438, Aug. 2018.

U. R. Muhammad, M. Svanera, R. Leonardi, and S. Benini, “Hair detection, segmentation, and hairstyle classification in the wild,” Image Vis. Comput., vol. 71, pp. 25–37, Mar. 2018.

D. Wang, X. Chai, H. Zhang, H. Chang, W. Zeng, and S. Shan, “A novel coarse-to-fine hair segmentation method,” in Face and Gesture 2011, 2011, pp. 233–238.

W. Guo and P. Aarabi, “Hair Segmentation Using Heuristically-Trained Neural Networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. 29, no. 1, pp. 25–36, Jan. 2018.

C. Rousset and P. Y. Coulon, “Frequential and color analysis for hair mask segmentation,” in 2008 15th IEEE International Conference on Image Processing, 2008, pp. 2276–2279.

P. Mehta, Dataset of around 800k images consisting of 1100 Famous Celebrities and an Unknown class to classify unknown faces: prateekmehta59/Celebrity-Face-Recognition-Dataset. 2019.

U. R. Muhammad, M. Svanera, R. Leonardi, and S. Benini, “Hair detection, segmentation, and hairstyle classification in the wild,” Image Vis. Comput., vol. 71, pp. 25–37, Mar. 2018.

Human-pose.mpi-inf.mpg.de. 2021. MPII Human Pose Database. [online] Available at: http://human-pose.mpi-inf.mpg.de [Accessed 25 July 2021].

M. Jalangan, “Skin Segmentation and Dominant Tone/Color Extraction,” Medium, 11-Sep-2018.

G. Bradski, “The OpenCV Library,” Dr Dobbs J. Softw. Tools, 2000.

‘D. E. King, “Dlib-ml: A Machine Learning Toolkit,” J. Mach. Learn. Res., vol. 10, pp. 1755–1758, 2009.

A. Pareek and W. Mar, “Optimized-RGB-To-ColorName/README.md at master •ayushoriginal/Optimized-RGB-To-ColorName,” github.com. [Online]. Available: https://github.com/ayushoriginal/Optimized-RGB-To- ColorName/blob/master/rgb2nearestcolor.py. [25 July 2021].

F. Chollet and others, “Keras,” 2015. [Online]. Available: https://keras.io. [Accessed 25 July 2021].

“ImageNet.” [Online]. Available: http://www.image-net.org/about-publication. [Accessed 25 July 2021].

S. Jain and T. Chawla, “‘Ayurvedic doshas identification using face and body image features’ – a review,” International Journal of Advanced Research in Computer Science, vol. 12, no. 3, pp. 19–22, 2021.




DOI: https://doi.org/10.26483/ijarcs.v12i4.6762

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