Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

An Assimilated Face Recognition System With effective Gender Recognition Rate

Author : K. Akhil Sreenivasulu 1 L. Guru Kumar 2 Dr. G. Harinatha Reddy 3

Date of Publication :8th March 2017

Abstract: Gender Recognition in Face Recognition System has prevalent applications in the fields of demographic data collection, video surveillance, security, retail advertising and marketing and it also plays a vital role on object recognition in robot research area, which has shown an intensive attention in the past few years.However, face recognition is still a challenging task since face images are easily confused by changes of the facial factors, such as illumination, pose, the different expressions, or glasses. The goal of this paper is to improve the effectiveness of gender recognition when there are affected(blurred) images in the dataset. We applied novel descriptor based COSFIRE filters to achieve the Gender Recognition[1] in the Face Recognition System. A COSFIRE filter is trainable, in that its selectivity is determined in an automatic configuration process that analyses a given prototype pattern of interest on FERET training set. Extensive experiments were conducted on the GENDER-FERET dataset which contains 474 training and 472 test samples and demonstrated robustness and effectiveness of the proposed model. It also outperforms an approach that relies on handcrafted features and an ensemble of classifiers.

Reference :

    1. George Azzopardi, Antonio Greco, Mario Vento, “Gender recognition from face images with trainable COSFIRE filters”, IEEE AVSS 2016, USA, Aug. 2016.
    2. Xipeng Yang, Jun Cheng, Wei Feng, Hong Liang, Zhengyao Bai, Dapeng Tao, “Face Recognition By Combining Cauchy Estimator and Discriminant Analysis”, IEEE International Conference on Real-time Computing and Robotics, 2016.
    3. G. Azzopardi, A. Greco, and M. Vento., “Gender recognition from face images using a fusion of SVM classifiers”, in International Conference Image Analysis and Recognition, pages 533–538. Springer, 2016.
    4. G. Azzopardi, L. Fernandez Robles, E. Alegre, and N. Petkov. “Increased generalization capability of trainable COSFIRE filters with application to machine vision”. In 23rd International Conference on Pattern Recognition (ICPR), 2016.
    5. C. B. Ng, Y. H. Tay, and B. M. Goi. “A review of facial gender recognition. Pattern Analysis and Applications”, 18(4):739– 755, 2015.
    6. M. Saraswathi,Dr. Krishnaveni Muthiah, “Improving Gender Recognition Rate in Face Recognition System Based On Linear Discriminant Analysis Technique”, International Journal of Advance Research in Computer Science and Management Studies, Apr. 2015, vol. 3, no. 4.
    7. P.Sasikala, N.Nirosha, E.Gurumoorthi, and J.VijayaBarathi, “Identification Of Gender And Face Recognition Using Adaboost And SVM Classifier”,vol 3, issue 11, pp. 9305-9312,Nov 2014.
    8. J. Bekios-Calfa, J. M. Buenaposada, and L. Baumela. “Robust gender recognition by exploiting facial attributes dependencies”. Pattern Recognition Letters, 36:228–234, 2014.
    9. G. Azzopardi, A. Rodriguez-Snchez, J. Piater, and N. Petkov. “A push-pull CORF model of a simple cell with antiphase inhibition improves SNR and contour detection”. PLoS ONE, 07, 2014.
    10. G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov. “Trainable COSFIRE filters for vessel delineation with application to retinal images. Medical Image Analysis”, 19(1):4657, 2014.
    11. G. Azzopardi and N. Petkov. “Ventral-stream-like shape representation: from pixel intensity values to trainable object selective COSFIRE models”. Frontiers in computational neuroscience, 8, 2014.
    12. Y. Taigman, M. Yang, M. A. Ranzato, and L. Wolf. Deepface: “Closing the gap to human-level performance in face verification”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1701–1708, 2014.
    13. V. Singh, V. Shokeen, and M. B. Singh. “Comparison of feature extraction algorithms for gender classification from face images”. In International Journal of Engineering Research and Technology, volume 2. ESRSA Publications, 2013
    14. J. E. Tapia and C. A. Perez., “Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape. Information Forensics and Security”, IEEE Transactions on, 8(3):488–499, 2013
    15. Y. Sun, X. Wang, and X. Tang. “Deep convolutional network cascade for facial point detection”, in Proceedings of theIEEE Conference on Computer Vision and Pattern Recognition, pages 3476–3483, 2013.
    16. E. Zhou, H. Fan, Z. Cao, Y. Jiang, and Q. Yin. “Extensive facial landmark localization with coarse-to-fine convolutional network cascade”. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 386–391, 2013.
    17. G. Azzopardi and N. Petkov. “Trainable COSFIRE filters for keypoint detection and pattern recognition”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(2):490–503, Feb 2013.
    18. G. Azzopardi and N. Petkov. “Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters”. Pattern Recognition Letters, 34:922–933, 2013
    19. G. Azzopardi and N. Petkov. “A shape descriptor based on trainable COSFIRE filters for the recognition of handwritten digits”. In Computer Analysis of Images and Patterns (CAIP 2013) Lecture Notes in Computer Science, 9– 16,2013
    20. C. Shan. “Learning local binary patterns for gender classification on real-world face images”. Pattern Recognition Letters, 33(4):431–437, 2012.
    21. G. Azzopardi and N. Petkov., “A CORF computational model of a simple cell that relies on lgn input outperforms the gabor function model”. Biological Cybernetics, pp 177–189, 2012.
    22. C. C. Chang and C. J. Lin. “LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology”, 2:27:1–27:27, 2011.
    23. L. A. Alexandre., “Gender recognition: A multiscale decision fusion approach”. Pattern Recognition Letters, 31(11):1422–1427, 2010.
    24. S. Milborrow and F. Nicolls. “Locating facial features with an extended active shape model”. In Computer Vision–ECCV 2008, pages 504–513. Springer, 2008.
    25. S. Baluja and H. A. Rowley. “Boosting sex identification performance”. International Journal of computer vision, 71(1):111–119, 2007.
    26. Z. Yang and H. Ai. “Demographic classification with local binary patterns”. In Advances in Biometrics, pages 464–473. Springer, 2007.
    27. S. Lazebnik, C. Schmid, and J. Ponce. “Beyond bags of features: Spatial pyramid matching for recognizing naturalscene categories”. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, CVPR ’06, pages 2169– 2178, USA, 2006. IEEE Computer Society.
    28. N. Dalal and B. Triggs. “Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition”, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886–893. IEEE, 2005.
    29. J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang. “Twodimensional pca: a new approach to appearance-based face representation and recognition”. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 26(1):131– 137, 2004.
    30. P. Viola and M. J. Jones. “Robust real-time face detection”. International journal of computer vision, 57(2):137–154, 2004.
    31. B. Moghaddam and M. Yang. “Learning gender with support faces”. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(5):707–711, 2002.
    32. T. Ojala, M. Pietik¨ainen, and T. M¨aenp¨a¨a. “Multi Resolution gray-scale and rotation invariant texture classification with local binary patterns’. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):971–987, 2002.
    33. A. Pasupathy and C. E. Connor. “Population coding of shape in area V4”. Nature Neuroscience, 5(12):1332–1338, DEC 2002.
    34. K. Mikolajczyk and C. Schmid. “An affine invariant interest point detector”. In Computer VisionECCV 2002, pages 128–142. Springer, 2002.
    35. P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss. “The FERET evaluation methodology for facerecognition algorithms”. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 22(10):1090–1104, 2000.
    36. R. Brunelli and T. Poggio. “Face recognition: Features versus templates”. IEEE Transactions on Pattern Analysis & Machine Intelligence, (10):1042–1052, 1993.

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