Local Directional Threshold based Binary Patterns for Facial Expression Recognition and Analysis

  • Authors

    • V Uma Maheswari
    • Vara Prasad
    • S Viswanadha Raju
    2018-09-25
    https://doi.org/10.14419/ijet.v7i4.6.20225
  • LDSM (Local Directional Standard Matrix), LDTBP (Local Dynamic Threshold based Binary Pattern), SVM (Support Vector Machine) Classifier, edge detection, facial expression recognition.
  • Abstract

    In this paper, proposing a novel method to retrieve the edge and texture information from facial images named local directional standard matrix (LDSM) and local dynamic threshold based binary pattern (LDTBP). LBP and LTP operators are used for texture extraction of an image by finding difference between center and surrounding pixels but they failed to detect edges and large intensity variations. Thus addressed such problems in proposed method firstly, calculated the LDSM matrix with standard deviation of horizontal and vertical pixels of each pixel. Therefore, values are encoded based on the dynamic threshold which is calculated from median of LDSM values of each pixel called LDTBP. In experiments used LFW facial expression dataset so used SVM classifier to classify the images and retrieved relevant images then measured in terms of average precision and average recall.

     

  • References

    1. [1] M. Kokare, B. N. Chatterji, and P. K. Biswas, “A survey on current content based image retrieval methods,†IETE J. Res., 48,(3)&(4), 2002, pp. 261–271.

      [2] Y. Liu, D. Zhang, G. Lu, and W.-Y.Ma, “A survey of content-based image retrieval with high-level semantics,†Pattern Recogn., 40,(1), Jan. 2007,pp. 262–282.

      [3] Y. Rui and T. S. Huang, “Image retrieval: Current techniques, promising directions and open issues,†J. Visual Commun. Image Represent.10,(1), Mar. 1999,pp. 39–62.

      [4] T. Ojala, M. Pietikainen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,†Pattern Recogn ,29,(1),Jan. 1996,pp. 51–59.

      [5] Xiaoyang Tan and Bill Triggs, “Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditionsâ€, IEEE Transactions on Image Processing, 19, (6), 2010, pp. 1635-1650.

      [6] Z. Lei, S. Liao, M. Pietikäinen, and S. Z. Li, “Face recognition by exploring information jointly in space, scale and orientation,†IEEE Trans. Image Process., 20, (1), Jan. 2011 pp. 247–256.

      [7] Bongjin Jun, Daijin Kim, “Robust face detection using local gradient patterns and evidence accumulationâ€, Pattern Recognition,45, 2012,pp.3304–3316.

      [8] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patternsâ€, IEEE Trans. Pattern Anal. Mach. Intell., 24,(7), Jul. 2002,pp. 971–987.

      [9] Subrahmanyam M, Maheshwari R. P, Balasubramanian R, “Local Maximum Edge Binary Patterns: A New Descriptor for Image Retrieval and Object Trackingâ€, Signal Processing, 2012, pp. 1467–1479.

      [10] S. Murala, R. P. Maheshwari, and R. Balasubramanian, “Local tetra patterns: a new feature descriptor for content-based image retrieval,†IEEE Transactions on Image Processing, 21, 5, 2012,pp. 2874–2886.

      [11] Subrahmanyam Murala · R. P. Maheshwari · R. Balasubramanian, "Directional local extrema patterns: a new descriptor for content based image retrieval", International Journal of Multimedia Information Retrieval, 1, (3), Mar. 2012pp. 191 - 203.

      [12] Santhosh Kumar Vipparthi, S. K. sagar, " Color Directional Local Quinary Patterns for Content Based Indexing and Retrieval", Human-centric Computing and Information Sciences, May. 2014,pp. 1 – 13.

      [13] Golla Varaprasad, Sachin Bharadwaj Sundra Murthy, “Detection of potholes in autonomous vehicleâ€, IET Intelligent Transport Systems, 2013, pp.543 – 549.

      [14] S. Viswanadha Raju, J. Sreedhar, “Query Processing for Content Based Image Retrievalâ€, International Journal of Soft Computing and Engineering, 1,(5), November 2011, pp.122 – 131.

      [15] Shiv Ram Dubey, Satish Kumar Singh, Rajat Kumar Singh, " Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval", IEEE Transactions on Image Processing, 25, (9), Sep. 2016, pp. 4018 – 4032.

      [16] Md. Mostafijur Rahman*, Shanto Rahman, “DTCTH: a discriminative local patterndescriptor for image classificationâ€, EURASIP Journal on Imageand Video Processing,30, 2017.

      [17] Anima Majumder, Laxmidhar Behera, “Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusionâ€,48,(1), 2018, pp. 103 – 114.

      [18] Byungyong Ryu; Adín Ramírez Rivera, “ Local DirectionalTernary Pattern for Facial Expression Recognitionâ€, IEEE Transactions on Image Processing,26, (12), 2017, pp. 6006 – 6018.

      [19] Md. Zia Uddin; Weria Khaksar; Jim Torresen, “ Facial Expression Recognition Using Salient Features and Convolutional Neural Networkâ€, IEEE Access, Vol.5, 2017, pp. 26146 – 26161.

      [20] Maryam Imani, Hassan Ghassemian, “GLCM, Gabor, and morphology profiles fusion for hyperspectral image classificationâ€, Electrical Engineering (ICEE), 2016 24th Proc. Int. Conf. IEEE, Shiraz, Iran, 10-12 May 2016, pp. 1-5.

      [21] Mahamed Hassan, Mohammed Hossny, “Skin lesion segmentation using Gray Level Co-occurance Matrixâ€, Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on, 9-12 Oct. 2016, Budapest, Hungary, pp. 820-825.

      [22] AnvitaBajpai, Kunal Chadha, “Real-time Facial Emotion Detection using Support Vector Machinesâ€, International Journal of Advanced Computer Science and Applications,2010, 1, (2), pp.37 – 40.

      [23] Hongzan Jiao, YanfeiZhong, “An Unsupervised Spectral Matching Classifier Based on Artificial DNA Computing for Hyperspectral Remote Sensing Imageryâ€, IEEE Transactions on Geoscience and Remote Sensing,2014, 52, (8), pp. 4524 – 4538.

      [24] Irene Kotsia and Ioannis Pitas, Senior Member, “Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machinesâ€, IEEE Transactions on Image Processing, 2007, 16, (1), pp. 172 – 187.

  • Downloads

  • How to Cite

    Uma Maheswari, V., Prasad, V., & Viswanadha Raju, S. (2018). Local Directional Threshold based Binary Patterns for Facial Expression Recognition and Analysis. International Journal of Engineering & Technology, 7(4.6), 17-22. https://doi.org/10.14419/ijet.v7i4.6.20225

    Received date: 2018-09-24

    Accepted date: 2018-09-24

    Published date: 2018-09-25