Gender Recogniotion Methods Useful in Mobile Authentication Applications

• Autorzy: Zofia Stawska , Piotr Milczarski
• Języki publikacji: EN

Abstrakty

EN

Soft biometrics methods that involve gender, age and ethnicity are still developed. Face recognition methods often rely on gender recognition. The same applies to the methods reconstructing the faces or building 2D or 3D models of the faces. In the paper, we conduct study on different set of gender recognition methods and their mobile applications. We show the advantages and disadvantages of that methods and future challenges to the researches. In the previous papers, we examined a range variety of skin detection methods that help to spot the face in the images or video stream. On acquiring faces, we focus on gender recognition that will allow us to create pattern to build 2D and 3D automatic faces models from the images. That will result also in face recognition and authentication, also. (original abstract)

Słowa kluczowe

EN

Biometry; Biometry methods; Authentication

PL

Biometria; Metody biometryczne; Uwierzytelnianie

• Czasopismo: Information Systems in Management
• Rocznik: 2016
• Tom: 5
• Numer: nr 2
• Strony: 248--259

Twórcy

autor

Zofia Stawska

• University of Lodz, Poland

autor

Piotr Milczarski

• University of Lodz, Poland

Bibliografia

• [1] Stawska Z., Milczarski P. (2013) Algorithms and Methods Used in Skin and Face Detection Suitable for Mobile Applications, ISIM: ISSN: 2084-5537, Vol. 2, No 3, pp. 227-238.
• [2] Milczarski P., Stawska Z. (2014) Complex Colour Detection Methods Used In Skin Detection Systems, ISIM: ISSN: 2084-5537, Vol. 3, No 1, pp. 40-52.
• [3] Viola P., Jones M. (2001) Rapid object detection using a boosted cascade of simple features In: Proc. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR'01), vol. 1, pp. 511-518.
• [4] Brunelli R., Poggio T. (1993) Face recognition: features versus templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042-1052.
• [5] Abdi H., Valentin D., Edelman B., O'Toole A.J. (1995) More about the difference between men and women: Evidence from linear neural network and principal component approach, Neural Comput. 7 (6), pp. 1160-1164.
• [6] Wiskott L., Fellous J.M., Krüger N., von der Malsburg C. (1997) Face recognition by elastic bunch graph matching, In: Sommer, G., Daniilidis, K., Pauli, J. (Eds.), 7th International Conference on Computer Analysis of Images and Patterns, CAIP'97, Kiel. Springer-Verlag, Heidelberg, pp. 456-463.
• [7] Cottrell G.W., Metcalfe J. (1990) EMPATH: Face, emotion, and gender recognition using holons, In: Lippmann, R., Moody, J.E., Touretzky, D.S. (Eds.), Proc. Advances in Neural Information Processing Systems 3. Morgan Kaufmann, pp. 564-571.
• [8] Golomb B.A., Lawrence D.T., Sejnowski T.J. (1990) SEXNET: A neural network identifies sex from human faces, In: Lippmann, R., Moody, J.E., Touretzky, D.S. (Eds.), Proc. Advances in Neural Information Processing Systems 3 (NIPS). Morgan Kaufmann, pp. 572-579.
• [9] Lyons M., Budynek J., Plante A., Akamatsu S. (2000) Classifying facial attributes using a 2-d Gabor wavelet representation and discriminant analysis, In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FG'00), IEEE, Grenoble, France, pp. 202-207.
• [10] Shakhnarovich G., Viola P.A., Moghaddam B. (2002) A unified learning framework for real time face detection and classification. In: Proc. Internat. Conf. on Automatic Face and Gesture Recognition (FGR'02). IEEE, pp. 14-21.
• [11] Sun Z., Bebis G., Yuan X., Louis S.J. (2002) Genetic feature subset selection for gender classification: A comparison study In: Proc. IEEE Workshop on Applications of Computer Vision (WACV'02), pp. 165-170.
• [12] Wu B., Ai H., Huang C. (2003) LUT-based Adaboost for gender classification In: Proc. Internat. Conf. on Audio and Video-based Biometric Person Authentication (AVBPA'03), Guildford, United Kingdom, pp. 104-110.
• [13] Sun N., Zheng W., Sun C., Zou C., Zhao L. (2006) Gender classification based on boosting local binary pattern In: Proc. 3rd Internat. Symposium on Neural Networks (ISNN'06), Chengdu, China, vol. 2, pp. 194-201.
• [14] Tamura S., Kawai H., Mitsumoto H. (1996) Male/female identification from 8 to 6 very low resolution face images, Pattern Recognition, 29 (2), pp. 331-335.
• [15] Fellous J.M. (1997) Gender discrimination and prediction on the basis of facial metric information, Vision Research, vol. 37, no. 14, pp. 1961-1973.
• [16] Moghaddam B., Yang M.H. (2002) Learning gender with support faces, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 24, no. 5, pp. 707-711.
• [17] Castrillon M., Deniz O., Hernandez D., Dominguez A. (2003) Identity and gender recognition using the encara real-time face detector in Conferencia de la Asociacin Espaola para la Inteligencia Artificial, vol. 3.
• [18] Buchala S., Loomes M.J., Davey N., Frank R.J. (2005) The role of global and feature based information in gender classification of faces: a comparison of human performance and computational models, International Journal of Neural Systems, vol. 15, pp. 121-128.
• [19] Jain A., Huang J., Fang S. (2005) Gender identification using frontal facial images in Multimedia and Expo, 2005. ICME 2005. IEEE International Conference on, p. 4.
• [20] Baluja S., Rowley H.A. (2007) Boosting sex identification performance, International Journal of Computer Vision, vol. 71, no. 1, pp. 111-119.
• [21] Fok T.H.C., Bouzerdoum A. (2006) A Gender Recognition System using Shunting Inhibitory Convolutional Neural Networks in The 2006 IEEE International Joint Conference on Neural Network Proceedings, pp. 5336-5341.
• [22] Aghajanian J., Warrell J., Prince S.J., Rohn J.L., Baum B. (2009) Patch-based within-object classification in 2009 IEEE 12th International Conference on Computer Vision, pp. 1125-1132.
• [23] Demirkus M., Toews M., Clark J.J., Arbel T. (2010) Gender classification from unconstrained video sequences in Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on, pp. 55-62.
• [24] Wang J.G., Li J., Lee C.Y., Yau W.Y. (2010) Dense SIFT and Gabor descriptors-based face representation with applications to gender recognition in Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on, no. December, pp. 1860-1864.
• [25] Alexandre L.A. (2010) Gender recognition: A multiscale decision fusion approach, Pattern Recognition Letters, vol. 31, no. 11, pp. 1422-1427.
• [26] Li B., Lian X.-C., Lu B.-L. (2011) Gender classification by combining clothing, hair and facial component classifiers, Neurocomputing, pp. 1-10.
• [27] Zheng J., Lu B.-Liang (2011) A support vector machine classifier with automatic confidence and its application to gender classification, Neurocomputing, vol. 74, no. 11, pp. 1926-1935.
• [28] Shan C. (2012) Learning local binary patterns for gender classification on real-world face images, Pattern Recognition Letters, vol. 33, no. 4, pp. 431-437.
• [29] Ojala T., Pietikäinen M., Harwood D. (1996) A comparative study of texture measures with classification based on featured distributions, Pattern Recognition, 29 (1), pp. 51-59.
• [30] Ojala T., Pietikäinen M., Mäenpää T. (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Machine Intell. 24 (7), pp. 971-987.
• [31] Lowe D.G. (2004) Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110.
• [32] Phillips P.J., Moon H., Rizvi S.A., Rauss P.J. (2000) The FERET evaluation methodology for face-recognition algorithms Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 10, pp. 1090-1104.
• [33] Gao W., Cao B., Shan S., Chen X. (2008) The CAS-PEAL large-scale Chinese face database and baseline evaluations Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, vol. 38, no. 1, pp. 149-161.
• [34] Huang G.B., Ramesh M., Berg T., Learned-Miller E. (2007) Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, University of Massachusetts Amherst Technical Report 07, vol. 49, pp. 1-11.
• [35] Ricanek K., Tesafaye T. (2006) Morph: A longitudinal image database of normal adult age-progression in Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on, pp. 341-345.
• [36] Mäkinen E., Raisamo R. (2008) An experimental comparison of gender classification methods, Pattern Recognition Letters 29, pp. 1544-1556.
• [37] Han X., Ugail H., Palmer I. (2009) Gender Classification Based on 3D Face Geometry Features Using SVM in CyberWorlds, 2009. CW'09. International Conference on, pp. 114-118.
• [38] Milczarski P., Kompanets L., Kurach D. (2010) An Approach to Brain Thinker Type Recognition Based on Facial Asymmetry in L. Rutkowski et al. (Eds.): ICAISC 2010, Part I, LNCS 6113, pp. 643-650. Springer, Heidelberg.
• [39] Kompanets L., Milczarski P., Kurach D. (2013) Creation of the fuzzy three-level adapting Brainthinker in Human System Interaction (HSI), 2013 The 6th International Conference on DOI: 10.1109/HSI.2013.6577865, pp. 459-465.