Dileep K. Yadav

Work place: Department of Computer Science Engineering, Galgotias University, Gautam Budh Nagar, U.P.

E-mail: dileep252000@gmail.com


Research Interests: Computer Vision, Image Processing


Dr. Dileep Kumar Yadav, received the Engineering degree (B. TECH. in Computer Science & Engineering) from Uttar Pradesh Technical University, Lucknow, UP, India in 2006 and Masters degree (M.TECH. in Computer Science & Technology) from School of Computer & Systems Sciences, Jawaharlal Nehru University, New Delhi, India in 2012. Dr. Yadav has earned Ph.D. (Computer Science & Technology) degree from School of Computer & Systems Sciences, Jawaharlal Nehru University New Delhi, India. His primary research interests are in image processing and computer vision. He is Sun Certified Java Programmer for Platform 1.5 (SCJP 1.5). He has more than 6 years of working experience in industry as well as academic. He has published many research articles (reputed Journals, IEEE International conferences, Springer –LNCS and National conferences) and some journal articles are in the under review.

Author Articles
Extraction of Facial Features for Detection of Human Emotions under Noisy Condition

By Mritunjay Rai R. K. Yadav Agha A. Husain Tanmoy Maity Dileep K. Yadav

DOI: https://doi.org/10.5815/ijem.2018.05.05, Pub. Date: 8 Sep. 2018

Affirmation of human faces out of still pictures or picture progressions is an as of now making research field. There are an extensive variety of engagements for structures adjusting to the issue of face limitation and affirmation e.g. exhibit based video coding, face conspicuous confirmation for security structures, look area, and human-PC connection. The acknowledgment and region of the face, and furthermore the extraction of facial features from the photos, are fundamental. In view of assortments in illumination, establishment, visual point and outward appearances, the issue becomes complicated. This paper presents a novel method to extract human facial features for the detection of human emotions (such as “sad”, “happy”, “sorrow” etc.) under noisy conditions. This whole work constitutes better working of a video surveillance system. For detection and extraction of facial features simple formulae are used to represent skin color models depending on the range of HSV (Hue, Saturation, Value) values used for the detection of human skin. Here HSV color model is used because it is fast as well as compatible with human color perception. Additionally, implementation of Probability Neural Network (PNN) enhances the working of the surveillance system. Utilization of PNN expands the ability of surveillance framework as it can give the yield image regardless of whether the information image contains noise in it. The proposed algorithm for the entire task is developed using MATLAB software along with suitable Image Processing Toolbox (IPT).

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