Performance analysis of frequency domain based feature extraction techniques for facial expression recognition

Facial Expression Recognition is a vital topic for research in current scenario which has many applications as machine based HR interviews and human-machine interaction. Facial Expression recognition is applied for identification of person using face of a person. Researchers have proposed many research techniques for facial expression recognition but still accuracy, illumination and occlusion are the research issues which have to improve. Key Research issue of facial expression is improving the accuracy of system which is measured in term of recognition rate. Feature extraction is the main stage on which accuracy depends for facial expression recognition. In this paper we have analyzed different feature extraction technique in frequency domain as Discrete Wavelet Transform, Discrete Cosine Transform feature extraction technique, Gabor filter and different feature reduction technique developed so far and future aspects.

[1]  Natarajan Ahmed.  DISCRETE COSINE TRANSFORMS , 2009 .

[2]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[3]  Neeta Nain,et al.  A Hybrid Method of Feature Extraction for Facial Expression Recognition , 2011, 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems.

[4]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[5]  Shan Wang,et al.  Wavelet Decomposition and Adaboost Feature Weighting for Facial Expression Recognition , 2011, 2011 International Conference on Control, Automation and Systems Engineering (CASE).

[6]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[7]  John Moriarty,et al.  The impact of image block size on face feature extraction using Discrete Cosine Transform , 2010, Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon).

[8]  Shubh Lakshmi Agrwal,et al.  New Gabor-DCT Feature Extraction Technique for Facial Expression Recognition , 2015, 2015 Fifth International Conference on Communication Systems and Network Technologies.

[9]  KamleshLakhwani Shubhlakshmi Agrwal ShilpaChoudhary.  An Efficient Hybrid Technique Of Feature Extraction For Facial Expression Recognition Using Adaboost Classifier , 2012 .

[10]  Michel Valstar,et al.  Automatic Facial Expression Analysis , 2015 .

[11]  N. Ahmed,et al.  Discrete Cosine Transform , 1996 .

[12]  Donglin Wang,et al.  Research on a method of facial expression recognition , 2009, 2009 9th International Conference on Electronic Measurement & Instruments.

[13]  Sandeep K. Gupta,et al.  A new Gabor wavelet transform feature extraction technique for ear biometric recognition , 2014, 2014 6th IEEE Power India International Conference (PIICON).

[14]  Boualem Boashash,et al.  A human identification technique using images of the iris and wavelet transform , 1998, IEEE Trans. Signal Process..

[15]  Thomas S. Huang,et al.  Features and fusion for expression recognition — A comparative analysis , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Shi Dongcheng,et al.  The method of facial expression recognition based on DWT-PCA/LDA , 2010, 2010 3rd International Congress on Image and Signal Processing.

[17]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[18]  Jun Ou,et al.  Automatic Facial Expression Recognition Using Gabor Filter and Expression Analysis , 2010, 2010 Second International Conference on Computer Modeling and Simulation.

[19]  Shubh Lakshmi Agrwal,et al.  Improved invisible watermarking technique using IWT-DCT , 2016, 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO).

[20]  Margaret Lech,et al.  Facial Expression Recognition Using Neural Networks and Log-Gabor Filters , 2008, 2008 Digital Image Computing: Techniques and Applications.