Refine your search
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Upadhyay, Aditi
- Face Identification and Verification using Hidden Markov Model with Maximum Score Approach
Abstract Views :167 |
PDF Views:0
Authors
Affiliations
1 Department of Electronics and Communication Engineering, School of Engineering& Technology Jaipur National University, Jaipur - 302015, Rajasthan, IN
1 Department of Electronics and Communication Engineering, School of Engineering& Technology Jaipur National University, Jaipur - 302015, Rajasthan, IN
Source
Indian Journal of Science and Technology, Vol 10, No 47 (2017), Pagination:Abstract
Objectives/Background: To analyse image using highest score estimation which will help to identify the accuracy in term of recognition rate with different likelihood. Methods/Statistical Analysis: To perform the accuracy measurement, first we initialize the HMM to find out the uniform segmentation and then extracting the feature of image by parameter initialization and subsequently train the feature image using Viterbi algorithm. Further, input image is converted into super and embedded state and transformation matrix is evaluated using maximum score estimation for each face. After that we check for maximum score HMM parameters if found ‘YES’ or go for some new feature extraction if found ‘NO’ condition. Findings: In this paper we try to find out the rate of recognition for different approaches using different class and compare it to achieve the best classification performance with twenty five facial images. Maximum score approach has been widely reported in many literature but only for the verification purpose. But, here we try to interpret the highest level of maximum score HMM by considering different class of feature of face which will also be used for identification and verification application. We also try to highlight that this algorithm brings highest process of recognition rate with massive proficiency to deal with new data and that makes it unique from other approach. Novelty/Improvement: This paper chooses discriminative feature extraction for face recognition. An experimental result concluded significant changes in term of improvement with respect to other existing conventional HMM based recognition and reflects better accuracy and efficient capability using proposed approaches. Applications: The application domain in face recognition includes surveillance, biometric, home appliances because of its richness. It is also used for access control like PC face based login, PC cameras beyond the password and physical security.Keywords
Discriminative Feature Extraction, Face Identification, Face Verification, Pattern Recognition, HMM- Nutritional Status of Pre-School Children Residing in Western Rajasthan
Abstract Views :212 |
PDF Views:0
Authors
Affiliations
1 Department of Food and Nutrition, College of Home Science, Swami Keshwanand Rajasthan Agricutural University, Bikaner (Rajasthan), IN
1 Department of Food and Nutrition, College of Home Science, Swami Keshwanand Rajasthan Agricutural University, Bikaner (Rajasthan), IN
Source
Food Science Research Journal, Vol 10, No 1 (2019), Pagination: 37-41Abstract
Rajasthan being a state of northern India is well known for its hyper arid partial irrigated agro-climatic zone. Along with vivid cultural practices this zone is a place arid horticulture which is suitable for cultivation of arid foods. Arid foods are nutrient dense foods especially micronutrients. Despite of such advantageous foods pre-school malnutrition is still prevailing in Rajasthan. A multi-staged cross-sectional study carried out in Bikaner east (a legislative constituency of Bikaner district situated in western Rajasthan) to estimate the prevalence of malnutrition among pre-school children (24- 71 months) studying in private and government schools of the district. Results revealed that out of 200 children belonging to private schools, 21 per cent were suffering from stunting, 55 per cent were wasted and 63 per cent were underweight. While children from government school (200) reported 49 per cent underweight children, 18 per cent stunting and 31 per cent were wasted. Obesity and overweight was also evident among the subjects. Dietary assessment showed that children were consuming adequate amount of milk and milk products, cereals, ischolar_mains and tubers but daily intake of fruits, green leafy vegetable and pulses was found to be low. Nutrient intake of iron, β-carotene, calcium, zinc and vitamin C was found to be low as compared to the daily nutrient recommendation for the age group. This implied that children may have micronutrient deficiencies which could be a serious issue. Results also points out the conclusion that rural influence, lower socio-economic condition, higher birth order, lower birth interval, maternal health, literacy level of parents, agricultural diversity and faulty feeding habits have adverse effects on nutritional status of children. Strategies should be implemented to educate parents and other child care givers to efficiently utilize the available food resources and nurturing practices to improve the nutritional status of their pre-school children.Keywords
Pre-Schoolers, Malnutrition, Obesity, Stunting, Wasting, Underweight, Nutrients.References
- Gopalan, C., Rama Sastri, B. V., Balasubramanian, S. C., Narasinga Rao, B. S., Deosthale, Y. G. and Pant, K.C. (1989). Nutritive value of Indian foods. Revised and updated Edition. National Institute of Nutrition, Indian Council of Medical Research, Hyderabad.
- ICMR (2008). Nutrient requirements and recommended dietary allowances for Indian. A report of the expert group of the Indian council of medical research, New Delhi, India.
- ICMR (2010). Nutrient requirements and recommended allowances for Indian. National Institute of Nutrition, Hyderabad, India.
- Khadilkar, V. V., Khadilkar, A. V., Cole, T. J., Chiplonkar, T.J., and Pandit, D. (2010).Overweight and obesity prevalence and body mass index trends in Indian children. Internat. J. Pediatric Obesity, 6:216-224.
- National Family Health Survey (NFHS 3), India (2006). International institute of population sciences. Mumbai (M.S.) India.
- National Family Health Survey (NFHS 4), India (2016). International institute of population sciences. Mumbai (M.S.) India.
- NCHS (1983). Nutrition monitoring and assessment. Edited by Gopaldas T. Ans Seshadri, S. Oxford University Press. UNICEF, New Delhi, India.
- NCHS (1990). Nutrition monitoring and assessment. Edited by Gopaldas T. Ans Seshadri, S. (1987). Oxford University Press. UNICEF, New Delhi, India.
- Ranjani, H., Mehreen, T.S. Pradeepa, R., Mohan, R., Anjana, Garg, R., Anand, K. and Mohan, V. (2016).Epidemiology of childhood overweight and obesity in India: A systematic review. Indian J. Med. Res., 143(2): 160–174.
- Sachdev, H.P.S. (1996). Assessing child malnutrition: some basic issues. Nutr, Foundation India, 16(4): 1-5.
- Shoeps, D.O., Abreu, L.C.D.,Valenti, V.E., Nascimento, V.G., Oliveira, A.G.D., Gallo, P.L., Wajnsztejn, R. and Leone, C. (2011). Nutritional status of pre-school children from low income families. Nutr J, 10: 43.
- Singh, M.B., Fotedar, R., Lakshaminarayana, J. and Anand, P.K. (2010). Studies on the nutritional status of children aged 0-5 years in a drought affected desert area of Western Rajasthan, India. Public Health Nutr., 9(8):961-967.
- Victora, C.G., Adair, L., Fall, C., Hallal, P.C., Martorell, R., Richter, L. and Sachdev, H.S. (2008). Maternal and child undernutrition: consequences for adult health and human capital. The Lancet, 371 (9609): 340–357.
- WHO (2008). WHO child growth standards: Training course on child growth assessment, interpreting growth indicators. World Health Organization, Geneva.
- Zou, Y., Zhang, R. and Zhou, B. (2015). A comparison study on the prevalence of obesity and its associated factors among city, township and rural area adults in China. BMJ Open, 5: 7.
- WEBLIOGRAPHY
- https://www.unicef.org/
- UNICEF. WHO, World Bank Group (2017).Levels and trends in child Malnutrition. available at http://www.who.int/nutgrowthdb/jme_brochure2016.pdf.