Abstract
This paper introduces a visual-based system, which can count the number of viewers and recognize their gender in front of an electronic billboard in real-time video streams. The viewers actually watching an advertisement are captured via frontal face detection techniques. To count the number of viewer precisely, the problem of occlusions between viewers is tackled. Besides, a complementary set of features is extracted from the torso of a viewer due to the fact that the part of the body contains relatively rich discriminative information than other body parts. In addition, for conducting robust viewer recognition, an online classifier trained by AdaBoost is developed. To recognize the gender of the counted viewers, an approach based on spatiotemporal probabilistic framework is proposed. Our experimental results demonstrate the robustness of the proposed system for the viewer counting and gender recognition tasks.
Similar content being viewed by others
References
Andreu Y, Mollineda RA, García-Sevilla P (2009) Gender recognition from a partial view of the face using local feature vectors. LNCS 5524:481–488
Balci K, Atalay V (2002) PCA for gender estimation: which eigenvectors contribute? Proc IEEE ICPR 3:363–366
Belongie S, Malik J, Puzicha J (2002) Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell 24(24):509–522
Bookstein FL (1989) Principal warps: thin-plate splines and decomposition of deformations. IEEE Trans Pattern Anal Mach Learn 11(6):567–585
Cao L, Dikmen M, Fu Y, Huang TS (2008) Gender recognition from body. ACM international conference on Multimedia session 2:725–729
Chan AB, Liang ZS, Vasconcelos N (2008) Privacy preserving crowd monitoring: counting people without people models or tracking. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, June 2008
Cho S-Y, Chow TWS, Leung C-T (1999) A neural-based crowd estimation by hybrid global learning algorithm. IEEE Trans Syst Man Cybern—Part B, 29(4), August 1999
Collins R, Lipton A, Fujiyoshi H, Kanade T (2001) Algorithms for cooperative multisensor surveillance. Proc IEEE 89(10):1456–1477
Fang Y, Wang Z (2008) Improving LBP features for gender classification. Proc International Conference on Wavelet Analysis and Pattern Recognition 1:373–377
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. Proc. Int’l Conf. on Machine Learning pp 148–156
Gallagher AC, Chen T (2009) Understanding images of groups of people. Proc IEEE CVPR pp 256–263
Goh R, Liu L, Liu X, Chen TH (2005) The CMU Face In Action (FIA) Database. Proc IEEE Analysis and Modeling of Faces and Gestures pp 255–263
Guo JM, Lin CC, Nguyen HS (2010) Face gender recognition using improved appearance-based average face difference and support vector machine. Proc. IEEE International Conference on System Science and Engineering, pp 637–640
Haritaoglu I, Harwood D, Davis LS (2000) W4: real-time surveillance of people and their activities. IEEE Trans Pattern Anal Mach Intell 22(8), August 2000
Huang C, Ai H, Li Y, Lao S (2007) High-performance rotation invariant multiview face detection. IEEE Trans Pattern Anal Mach Intell 29(4):671–686
Kienzle W, Bakir G, Franz M, Scholkopf B (2005) Face detection—efficient and rank deficient. Adv Neural Inf Process Syst 17:673–680
Lapedriza A, Masip D, Vitria J (2005) Are external face features useful for automatic face classification? Proc IEEE CVPR pp 151–158, June 2005
Lapedriza A, Marin-Jimenez MJ, Vitria J (2006) Gender recognition in non controlled environments. Proc IEEE ICPR 3:834–837
Lee DD, Seung HS (1999) Learning the parts of objects with nonnegative matrix factorization. Nature 401:788–791
Lian HC, Lu BL (2007) Multi-view gender classification using multi-resolution local binary patterns and support vector machines. Int J Neural Syst 17:479–487
Lin H, Lu H, Zhang L (2006) A new automatic recognition system of gender, age and ethnicity. The Sixth World Congress on Intelligent Control and Automation 2:9988–9991
Lu H, Lin H (2007) Gender recognition using adaboosted feature. Proc International Conference on Natural Computation 2:646–650
Lu H, Huang Y, Chen Y, Yang D (2003) Automatic gender recognition based on pixel-pattern-based texture feature. J Real-Time Image Proc 3:109–116
Mayo M, Zhang E (2008) Improving face gender classification by adding deliberately misaligned faces to the training data. Proc Int’l Conf Image and Vision Computing pp 1–5, Nov. 2008
Moghaddam B, Yang MH (2000) Gender classification with support vector machines. Proc IEEE Automatic Face and Gesture Recognition, pp 306–311
Nikolaus (2007) Learning the parts of objects using non-negative matrix factorization. Term Paper, Feb. 2007
Osuna E, Freund R (1997) An improved training algorithm for support vector machine. Proc IEEE Workshop on Neural Networks for Signal Processing pp 276–285
Otsu N (1979) A threshold selection method from Gray-Level histograms. IEEE Trans Syst Man Cybern 9(1):62–66
Phillips PI, Wechsler H, Huang I, Rauss P (1998) The FERET database and evaluation procedure for face recognition algorithms. J Image Vis Comput 16(5):295–306
Phillips PJ, Moon H, Rizvi SA, Rauss PI (2000) The FERET evaluation methodology for face recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22:1090–1104
Regazzoni CS, Tesei A (1996) Distributed data fusion for real-time crowding estimation. Signal Process 53:47–63
Rodrigo V, Javier RDS, Mauricio C (2006) Gender classification of faces using Adaboost. Proc CIARP 4225:68–78
Schapire RE, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336
Shen BC, Chen CS, Hsu HH (2009) Fast gender recognition by using a shared-integral-image approach. Proc IEEE ICASSP pp 521–524
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. Proc IEEE CVPR 1:511–518
Viola P, Jones MJ, Snow D (2003) Detecting pedestrians using patterns of motion and appearance. International Conference on Computer Vision
WANG Y, AI H, WU B, HUANG C (2004) Real time facial expression recognition with Adaboost. Proc IEEE ICPR 3:926–929
Wu B, Ai H, Huang C (2003) LUT-based Adaboost for gender classification. Audio- and Video-Based Biometric Person Authentication 2688:104–110
Wu B, Ai H, Huang C (2004) Facial image retrieval based on demographic classification. Proc IEEE ICPR 3:914–917
Yedidia JS, Freeman WT, Weiss Y (2001) Generalized belief propagation. In: Leen TK, Dietterich TG, Tresp V (eds) Advances in neural information processing systems, 13. MIT, Cambridge, pp 689–695
Yedidia JS, Freeman WT, Weiss Y (2001) Understanding belief propagation and its generalizations. Proc Int’l Conf on Artificial Intelligence
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chen, DY., Lin, KY. Face-based multiple instance analysis for smart electronics billboard. Multimed Tools Appl 59, 221–240 (2012). https://doi.org/10.1007/s11042-011-0746-9
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-011-0746-9