Soft Biometrics for a Socially Assistive Robotic Platform

References

[1] (May 2014), https://community.aldebaran-robotics.com/doc/ 1-14/index.html Search in Google Scholar

[2] (May 2014), http://www.faceaginggroup.com/morph/ Search in Google Scholar

[3] Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. Pattern Analysis and Machine Intelligence, IEEE Transactions on 19(7), 711–720 (Jul 1997) 10.1109/34.598228Search in Google Scholar

[4] Bemelmans, R., Gelderblom, G.J., Jonker, P., De Witte, L.: Socially assistive robots in elderly care: A systematic review into effects and effectiveness. Journal of the American Medical Directors Association 13(2), 114–120 (2012) 10.1016/j.jamda.2010.10.002Search in Google Scholar PubMed

[5] Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. pp. 144–152. COLT ’92, ACM, New York, NY, USA (1992), http://doi.acm.org/ 10.1145/130385.130401 Search in Google Scholar

[6] Brey, P.: Freedom and privacy inambient intelligence. Ethics and Information Technology 7(3), 157–166 (2005) 10.1007/s10676-006-0005-3Search in Google Scholar

[7] Brunelli, R., Poggio, T.: Hyberbf networks for gender classification (1995) Search in Google Scholar

[8] Carcagni, P., Del Coco, M., Mazzeo, P., Testa, A., Distante, C.: Features descriptors for demographic estimation: a comparative study. In: Workshop on Video Analytics for Audience Measurement in Retail and Digital Signage (VAAM). In Conjunction with the 22nd International Conference on Pattern Recognition ICPR 2014 (Aug 2014), accepted for pubblication 10.1007/978-3-319-12811-5_5Search in Google Scholar

[9] Castrillón, M., Déniz, O., Guerra, C., Hernández, M.: Encara2: Real-time detection of multiple faces at different resolutions in video streams. Journal of Visual Communication and Image Representation 18(2), 130–140 (2007) 10.1016/j.jvcir.2006.11.004Search in Google Scholar

[10] Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), software available at http://www.csie. ntu.edu.tw/~cjlin/libsvm 10.1145/1961189.1961199Search in Google Scholar

[11] Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: Wld: A robust local image descriptor. Pattern Analysis and Machine Intelligence, IEEE Transactions on 32(9), 1705– 1720 (Sept 2010) 10.1109/TPAMI.2009.155Search in Google Scholar PubMed

[12] Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995), http://dx.doi.org/10.1023/A% 3A1022627411411 Search in Google Scholar

[13] Cottrell, G.W., Metcalfe, J.: Empath: Face, emotion, and gender recognition using holons. In: Advances in Neural Information Processing Systems. pp. 564–571 (1990) Search in Google Scholar

[14] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. vol. 1, pp. 886–893 vol. 1 (June 2005) Search in Google Scholar

[15] Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (Aug 1997) 10.1006/jcss.1997.1504Search in Google Scholar

[16] Fukai, H., Nishie, Y., Abiko, K., Mitsukura, Y., Fukumi, M., Tanaka, M.: An age estimation system on the aibo. In: Control, Automation and Systems, 2008. ICCAS 2008. International Conference on. pp. 2551–2554 (Oct 2008) Search in Google Scholar

[17] Golomb, B.A., Lawrence, D.T., Sejnowski, T.J.: Sexnet: A neural network identifies sex from human faces. In: NIPS. pp. 572–579 (1990) Search in Google Scholar

[18] H., A., D., V., B., E., AJ., O.: More about the difference between men and women: evidence from linear neural networks and the principal-component approach. Neural Comput. 7(6), 1160 – 1164 (1995) Search in Google Scholar

[19] Hsu, C.W., Chang, C.C., Lin, C.J., et al.: A practical guide to support vector classification (2003) Search in Google Scholar

[20] Hsu, C.W., Lin, C.J.: A comparison of methods formulticlass support vector machines. Neural Networks, IEEE Transactions on 13(2), 415–425 (Mar 2002) 10.1109/72.991427Search in Google Scholar PubMed

[21] Jain, A.K., Dass, S.C., Nandakumar, K.: Soft biometric traits for personal recognition systems. In: Biometric Authentication, pp. 731–738. Springer (2004) 10.1007/978-3-540-25948-0_99Search in Google Scholar

[22] Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Souli, F., Hrault, J. (eds.) Neurocomputing, NATOASI Series, vol. 68, pp. 41–50. Springer Berlin Heidelberg (1990), http://dx.doi.org/10.1007/978-3-642-76153-9_5 10.1007/978-3-642-76153-9_5Search in Google Scholar

[23] Lee, M.W., Kwak, K.C.: Performance comparison of gender and age group recognition for human-robot interaction. International Journal of Advanced Computer Science & Applications 3(12) (2012) 10.14569/IJACSA.2012.031234Search in Google Scholar

[24] Liu, L., Liu, J., Cheng, J.: Age-group classification of facial images. In: Machine Learning and Applications (ICMLA), 2012 11th International Conference on. vol. 1, pp. 693–696 (Dec 2012) 10.1109/ICMLA.2012.129Search in Google Scholar

[25] Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (Nov 2004) 10.1023/B:VISI.0000029664.99615.94Search in Google Scholar

[26] Luo, R., Chang, L.W., Chou, S.C.: Human age classification using appearance images for human-robot interaction. In: Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE. pp. 2426–2431 (Nov 2013) 10.1109/IECON.2013.6699511Search in Google Scholar

[27] Luo, R.C., Wu, X.: Real-time gender recognition based on 3d human body shape for human-robot interaction. In: Proceedings of the 2014 ACM/IEEE international conference on Human-robot interaction. pp. 236–237. ACM (2014) 10.1145/2559636.2563679Search in Google Scholar

[28] Lyons, M.J., Budynek, J., Plante, A., Akamatsu, S.: Classifying facial attributes using a 2-d gabor wavelet representation and discriminant analysis. In: Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on. pp. 202–207 (2000) Search in Google Scholar

[29] Mäkinen, E., Raisamo, R.: An experimental comparison of gender classification methods. Pattern Recognition Letters 29(10), 1544 – 1556 (2008), http://www.sciencedirect.com/science/ article/pii/S0167865508001116 Search in Google Scholar

[30] Martiriggiano, T., Leo, M., D’Orazio, T., Distante, A.: Face recognition by kernel independent component analysis. In: Ali, M., Esposito, F. (eds.) Innovations in Applied Artificial Intelligence, Lecture Notes in Computer Science, vol. 3533, pp. 55–58. Springer Berlin Heidelberg (2005), http://dx.doi.org/10.1007/ 11504894_7 10.1007/11504894_7Search in Google Scholar

[31] McColl, D., Zhang, Z., Nejat, G.: Human body pose interpretation and classification for social human-robot interaction. International Journal of Social Robotics 3(3), 313–332 (2011) 10.1007/s12369-011-0099-6Search in Google Scholar

[32] Moore, D.: Computers and people with autism. Asperger Syndrome pp. 20–21 (1998) Search in Google Scholar

[33] Moore, D., McGrath, P., Thorpe, J.: Computer-aided learning for peoplewith autism - a framework for research and development. Innovations in Education and Teaching International 37(3), 218– 228 (2000) Search in Google Scholar

[34] Phillips, P., Moon, H., Rizvi, S., Rauss, P.: The feret evaluation methodology for face-recognition algorithms. Pattern Analysis and Machine Intelligence, IEEE Transactions on 22(10), 1090– 1104 (Oct 2000) 10.1109/34.879790Search in Google Scholar

[35] Saatci, Y., Town, C.: Cascaded classification of gender and facial expression using active appearance models. In: Automatic Face and Gesture Recognition, 2006. FGR 2006. 7th International Conference on. pp. 393–398 (April 2006) Search in Google Scholar

[36] Sakarkaya, M., Yanbol, F., Kurt, Z.: Comparison of several classification algorithms for gender recognition from face images. In: Intelligent Engineering Systems (INES), 2012 IEEE 16th International Conference on. pp. 97–101 (June 2012) 10.1109/INES.2012.6249810Search in Google Scholar

[37] Sun, N., Zheng, W., Sun, C., Zou, C., Zhao, L.: Gender classification based on boosting local binary pattern. In: Wang, J., Yi, Z., Zurada, J., Lu, B.L., Yin, H. (eds.) Advances in Neural Networks - ISNN 2006, Lecture Notes in Computer Science, vol. 3972, pp. 194–201. Springer Berlin Heidelberg (2006) 10.1007/11760023_29Search in Google Scholar

[38] Sun, Z., Bebis, G., Yuan, X., Louis, S.J.: Genetic feature subset selection for gender classification: A comparison study. In: In: IEEE Workshop on Applications of Computer Vision. pp. 165–170 (2002) Search in Google Scholar

[39] Tapus, A., Maja, M., et al.: Towards socially assistive robotics. International journal of the Robotics Society of Japan (JRSJ) 24(5), 576–578 (2006) 10.7210/jrsj.24.576Search in Google Scholar

[40] Tapus, A., Țăpuş, C., Matarić, M.J.: User?robot personality matching and assistive robot behavior adaptation for poststroke rehabilitation therapy. Intelligent Service Robotics 1(2), 169–183 (2008) 10.1007/s11370-008-0017-4Search in Google Scholar

[41] Tapus, A., Tapus, C., Mataric, M.J.: The use of socially assistive robots in the design of intelligent cognitive therapies for people with dementia. In: Rehabilitation Robotics, 2009. ICORR 2009. IEEE International Conference on. pp. 924–929. IEEE (2009) 10.1109/ICORR.2009.5209501Search in Google Scholar

[42] Ullah, I., Hussain, M.,Muhammad, G., Aboalsamh, H., Bebis, G., Mirza, A.: Gender recognition from face images with local wld descriptor. In: Systems, Signals and Image Processing (IWSSIP), 2012 19th International Conference on. pp. 417–420 (April 2012) Search in Google Scholar

[43] Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. vol. 1, pp. I–511. IEEE (2001) Search in Google Scholar

[44] Walker, J.H., Sproull, L., Subramani, R.: Using a human face in an interface. In: Proceedings of the SIGCHI conference on Human factors in computing systems. pp. 85–91. ACM (1994) 10.1145/191666.191708Search in Google Scholar

[45] Wayman, J.L.: Large-scale civilian biometrics system - issues and feasibility. In: Proceedings of the CardTech/SecureTech Government, Washington DC (1997) Search in Google Scholar

[46] Ylioinas, J., Hadid, A., Pietikainen, M.: Age classification in unconstrained conditions using lbp variants. In: Pattern Recognition (ICPR), 2012 21st International Conference on. pp. 1257– 1260 (Nov 2012) Search in Google Scholar

[47] Ylioinas, J., Hadid, A., Hong, X., Pietikäinen, M.: Age estimation using local binary pattern kernel density estimate. In: Petrosino, A. (ed.) Image Analysis and Processing - ICIAP 2013, Lecture Notes in Computer Science, vol. 8156, pp. 141–150. Springer Berlin Heidelberg (2013) 10.1007/978-3-642-41181-6_15Search in Google Scholar