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

Full Text (PDF, 689KB), PP.49-62

Views: 0 Downloads: 0

Author(s)

Mritunjay Rai 1,* R. K. Yadav 2 Agha A. Husain 1 Tanmoy Maity 1 Dileep K. Yadav 3

1. Department of MME, Indian Institute of Technology (ISM), Dhanbad, Jharkhand

2. Department of Electronics & Communication Engineering, SIET, Greater Noida, U.P.

3. Department of Computer Science Engineering, Galgotias University, Gautam Budh Nagar, U.P.

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2018.05.05

Received: 19 Jan. 2016 / Revised: 22 Nov. 2017 / Accepted: 8 Jan. 2018 / Published: 8 Sep. 2018

Index Terms

Facial features, human emotions, video surveillance system, PNN

Abstract

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).

Cite This Paper

Mritunjay Rai, R.K.Yadav, Agha A. Husain, Tanmoy Maity, Dileep K. Yadav,"Extraction of Facial Features for Detection of Human Emotions under Noisy Condition", International Journal of Engineering and Manufacturing(IJEM), Vol.8, No.5, pp.49-62, 2018. DOI: 10.5815/ijem.2018.05.05

Reference

[1]Qinsheng Du, Jian Zhao, “Research on the Two-Dimensional Face Image Feature Extraction Method,” 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization, pp. 251-254, 2012.

[2]Xi-wen Zhang, Michael R. Lyu , “Accurate extraction of human faces and their components from color digital images based on a hierarchical model,” 4th International Congress on Image and Signal Processing, pp. 1165-1174, 2011.

[3]Rein-Lien Hsu, Mohamed Abdel Mottaleb, Anil K. Jain, “IEEE Transactions on Pattern Analysis and Machine Intelligence,” pp. 696-706, Vol. 24, No. 5, May 2002.

[4]Rojana Kam-art, Thanapant Raicharoen,Varin Khera, “Face Recognition using feature Extraction based on Descriptive statistics of a image,” Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009.

[5]Hironori Takimoto, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu, “A Feature Extraction Method in Face Image for Personal Identification,” Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 1081-1084, July 16-20, 2003.

[6]C.Chen, S.P. Chiang, “Detection of Human Faces in colour images,”IEEE Proceedings- Visual Image signal Processing, Vol. 144, No. 6, pp.384-388, December 1997.

[7]L. Trujillo, G. Olague, R. Hammoud, B. Herna′ndez, Automatic feature localization in thermal images for facial expression recognition, in: CVPR ’05: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)—Workshops, IEEE Computer Society, Washington, DC, USA, p. 14, 2005.

[8]A. Tofighi, N. Khairdoost, S. A. Monadjemi, K. Jamshidi,"A Robust Face Recognition System in Image and Video", IJIGSP, vol.6, no.8, pp.1-11, 2014.DOI: 10.5815/ijigsp.2014.08.01

[9]M. Md. Maruf, P. Padma Polash, I. Md. Wahedul, and R. Siamak, "A Real-Time Face Recognition Approach from Video Sequence using Skin Color Model and Eigenface Method," in Electrical and Computer Engineering, 2006. CCECE '06. Canadian Conference on, 2006, pp. 2181- 2185.

[10]M. F. Valstar and M. Pantic, "Combined support vector machines and hidden markov models for modelling facial action temporal dynamics," Human–Computer Interaction, pp. 118-127, 2012.

[11]L. Zhong, Q. Liu, P. Yang, B. Liu, J. Huang and D. N. Metaxas, "Learning active facial patches for expression analysis," in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

[12]S. Tripathi, V. Sharma, S. Sharma, “Face detection using combined skin color detector and template matching method,” International Journal of Computer Application vol. 26, No. 7 pp:5–8, 2011.

[13]A. L. Yuille, D. S. Cohen, P.W. Hallinan, "Feature extraction from faces using deformable templates", Proc. of CVPR, 1989.

[14]A.Bhatia, S.Srivastava, A.Agarwal, “Face detection using fuzzy logic and skin color segmentation in images,” Emerging Trends in Engineering and Technology (ICETET), 3rd International Conference, IEEE, pp 225–228, 2010.

[15]Erik Hjelmas, Boon Kee Low, “Face Detection: A Survey,” Computer Vision and Image Understanding, 83, 236-274 April 2001.

[16]Toshiyuki Sakai, M. Nagao, Takeo Kanade, “Computer analysis and classification of photographs of human face,” First USA Japan Computer Conference, 1972.

[17]Craw, I., Ellis, H. and Lishman, “Automatic extraction of face feature”, Pattern Recog. Lett. 183-187 1987.

[18]Phil Brimblecombe, “Face detection using neural networks” Meng Electronic Engineering School of Electronics and Physical Sciences, University of Surrey.

[19]A. Lanitis, C. J. Taylor, and T. F. Cootes, “An automatic face identification system using flexible appearance models,” Image and Vision Computing, vol.13, no.5, pp.393-401, 1995.

[20]G.Yang, T.S.Huang, “Human face detection in a complex background,” Pattern Recognition vol. 27, No.1, pp. 53–63, 1994.

[21]H.Rowley,S. Baluja, T. Kanade, “Neural network-based face detection,” Pattern Analystics Machine Intelligence IEEE Transaction,vol. 20,No. 1, pp.23–38.

[22]Chun-Hung Lin and Ja-Ling Wu, “Automatic Facial Feature Extraction by Genetic Algorithms,” IEEE Transactions on Image Processing, Vol. 8, No. 6, pp. 834-845, June 1999.

[23]Shang-Hung Lin, Sun-Yuan Kung, “Face Recognition/Detection by Probabilistic Decision-Based Neural Network,” IEEE Transactions on Neural Networks, Vol. 8, No. 1, pp. 114-132, January 1997.

[24]Y. Liu, D. Zhang, G. Lu, and W. Ma., “A survey of content based image retrieval with high-level semantics,” Pattern Recognition, 40:262–282, 2007.

[25]S S. Tripathy, Priyank Saxena, S. S. Solanki, R. Sukesh Kumar, “PNN Implementation of Content Based Image Retrieval Using Descriptors Hierarchy,” International Conference on Content Based Image Retrieval, pp. 22-26, July 16-18, 2008.

[26]Y. Ming-Hsuan, D. J. Kriegman, and N. Ahuja, "Detecting faces in images: A Survey," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, pp. 34-58, 2002.