Gender detection using 3D anthropometric measurements by Kinect

• Autorzy: Camalan S. , Sengul G. , Misra S. , Maskeliunas R. , Damaševičius R.

Identyfikatory

DOI

10.24425/119568

• Języki publikacji: EN

Abstrakty

EN

Automatic gender detection is a process of determining the gender of a human according to the characteristic properties that represent the masculine and feminine attributes of a subject. Automatic gender detection is used in many areas such as customer behaviour analysis, robust security system construction, resource management, human-computer interaction, video games, mobile applications, neuro-marketing etc., in which manual gender detection may be not feasible. In this study, we have developed a fully automatic system that uses the 3D anthropometric measurements of human subjects for gender detection. A Kinect 3D camera was used to recognize the human posture, and body metrics are used as features for classification. To classify the gender, KNN, SVM classifiers and Neural Network were used with the parameters. A unique dataset gathered from 29 female and 31 male (a total of 60 people) participants was used in the experiment and the Leave One Out method was used as the cross-validation approach. The maximum accuracy achieved is 96.77% for SVM with an MLP kernel function.

Słowa kluczowe

EN

gender detection; Kinect sensor; anthropometrics; measurement; gender issues

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2018
• Tom: Vol. 25, nr 2
• Strony: 253--267
• Opis fizyczny: Bibliogr. 49 poz., fot., rys., tab., wykr., wzory

Twórcy

autor

Camalan S.

• Atilim University, Department of Information Systems Engineering, Incek, Ankara 06836, Turkey

autor

Sengul G.

• Atilim University, Department of Computer Engineering, Incek, Ankara 06836, Turkey

autor

Misra S.

• Atilim University, Department of Computer Engineering, Incek, Ankara 06836, Turkey
• Covenant University, Department of Electrical and Information Engineering, Ota 1023, Nigeria

autor

Maskeliunas R.

• Kaunas University of Technology, Department of Multimedia Engineering, Kaunas 51368, Lithuania

autor

Damaševičius R.

• Kaunas University of Technology, Department of Software Engineering, Kaunas 51368, Lithuania

Bibliografia

• [1] You, Q., Bhatia, S., Sun, T., Luo, J. (2014). The Eyes of the Beholder: Gender Prediction Using Images Posted in Online Social Networks. 2014 IEEE International Conference on Data Mining Workshop, Shenzhen, 1026-1030.
• [2] Zhang, J., Du, K., Cheng, R., Wei, Z., Qin, C., You, H., Hu, S. (2016). Reliable Gender Prediction Based on Users’ Video Viewing Behavior. 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, 649-658.
• [3] Duong, D., Tan, H., Pham, S. (2016). Customer gender prediction based on E-commerce data. 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE), Hanoi, 91-95.
• [4] Topaloglu, M., Ekmekci, S. (2017). Gender detection and identifying one’s handwriting with handwriting analysis. Expert Syst. Appl., 79, C, 236-243.
• [5] Dantcheva, A., Elia P., Ross, A. (2016). What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics. IEEE Transactions on Information Forensics and Security, 11(3), 441-467.
• [6] Abouelenien, M., Pérez-Rosas, V., Mihalcea, R., Burzo, M. (2017). Multimodal gender detection. 19th ACM International Conference on Multimodal Interaction (ICMI 2017). ACM, 302-311.
• [7] Seneviratne, S., Seneviratne, A., Mohapatra, P., Mahanti A. (2015). Your Installed Apps Reveal Your Gender and More! SIGMOBILE Mob. Comput. Commun. Rev., 18(3), 55-61.
• [8] Cao, L., Dikmen, M., Fu, Y., Huang, T.S. (2008). Gender recognition from body. 16th ACM International Conference on Multimedia (MM ’08), 725-728.
• [9] Guo, G.-D., Mu, G., Fu, Y. (2009). Gender from body: a biologically-inspired approach with manifold learning. 9th Asian Conference on Computer Vision, 236-245.
• [10] Wu, Q., Guo, G. (2014). Gender Recognition from Unconstrained and Articulated Human Body. The Scientific World Journal, Article ID 513240.
• [11] Lin, F., Wu, Y., Zhuang, Y., Long, X., Xu, W. (2016). Human gender classification: a review. Int. J. Biom., 8, 275-300.
• [12] Jalal, A., Kamal, S., Kim, D. (2015). Shape and Motion Features Approach for Activity Tracking and Recognition from Kinect Video Camera. 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, 445-450.
• [13] Farooq, A., Jalal, A., Kamal, S. (2015). Dense RGB-D Map-Based Human Tracking and Activity Recognition using Skin Joints Features and Self-Organizing Map. KSII Transactions on Internet and Information Systems, 9(3), 1856-1869.
• [14] Han, H., Otto, C., Liu, X., Jain, A.K. (2015). Demographic estimation from face images: Human vs. machine performance. IEEE Trans. Pattern Anal. Mach. Intell., 37, 1148-1161.
• [15] Andreu, Y., García-Sevilla, P., Mollineda, R.A. (2014). Face gender classification: A statistical study when neutral and distorted faces are combined for training and testing purposes. Image Vis. Comput., 32, 27-36.
• [16] Farina, G. L., Spataro, F., De Lorenzo, A., Lukaski, H.A. (2016). Smartphone Application for Personal Assessments of Body Composition and Phenotyping. Sensors, 16(12), 2163.
• [17] Riaz, Q., Vögele, A., Krüger, B., Weber, A. (2015). One Small Step for a Man: Estimation of Gender, Age and Height from Recordings of One Step by a Single Inertial Sensor. Sensors, 15(12), 31999-32019.
• [18] Scano, A., Chiavenna, A., Malosio, M., Tosatti, L.M. (2017). Kinect V2 Performance Assessment in Daily-Life Gestures: Cohort Study on Healthy Subjects for a Reference Database for Automated Instrumental Evaluations on Neurological Patients. Applied Bionics and Biomechanics.
• [19] Buffa, R., Mereu, E., Lussu, P., Succa, V., Pisanu, T., Buffa, F., Marini, E.A. (2015). New, Effective and Low-Cost Three-Dimensional Approach for the Estimation of Upper-Limb Volume. Sensors , 15, 12342-12357.
• [20] Skalski, A., Machura, B. (2015). Metrological Analysis Of Microsoft Kinect In The Context Of Object Localization. Metrol. Meas. Syst., 22(4), 469-478.
• [21] Fryar, C.D., Gu, Q., Ogden, C.L., Flegal, K.M. (2016). Anthropometric reference data for children and adults: United States, 2011-2014. National Center for Health Statistics. Vital Health Stat, 3(39), 7-9.
• [22] Sandygulova, A., Dragone, M., O’Hare, G.M.P. (2014). Real-time adaptive child-robot interaction: Age and gender determination of children based on 3d body metrics. The 23rd IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN, 826-831.
• [23] Yoo, J.H., Hwang, D., Nixon, M.S. (2005). Gender classification in human gait using support vector machine. Proc. of the 7th international conference on Advanced Concepts for Intelligent Vision Systems ACIVS, Springer, 5, 138-145.
• [24] Lee, L., Grimson, W.E.L. (2002). Gait analysis for recognition and classification. Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 155-162.
• [25] Makihara, Y., Mannami, H., Yagi, Y. (2010). Gait analysis of gender and age using a large-scale multiview gait database. Asian Conference on Computer Vision, Springer, 440-451.
• [26] Cao, L., Dikmen, M., Fu, Y., Huang, T.S. (2008). Gender recognition from body. Proc. of the 16th ACM international conference on Multimedia, ACM, 725-728.
• [27] Guo, G., Mu, G., Fu, Y. (2009). Gender from body: A biologically-inspired approach with manifold learning. Asian Conference on Computer Vision, Springer, 236-245.
• [28] Collins, M., Zhang, J., Miller, P., Wang, H. (2009). Full body image feature representations for gender profiling. 2009 IEEE 12th International Conference on, Computer Vision Workshops (ICCV Workshops), IEEE, 1235-1242.
• [29] Miyamoto, R., Aoki, R. (2015). Gender prediction by gait analysis based on time series variation on joint position. J. Syst. Cybern. Informatics, 13, 75-82.
• [30] Won, A.S., Yu, L., Janssen, J.H., Bailenson, J.N. (2012). Tracking gesture to detect gender. Proc. of the International Society for Presence Research Annual Conference, 24-26.
• [31] Adjeroh, D., Cao, D., Piccirilli, M., Ross, A. (2010). Predictability and correlation in human metrology. 2010 IEEE international workshop on Information forensics and security (WIFS), 1-6.
• [32] Robinette, K.M., Blackwell, S., Daanen, H., Boehmer, M., Fleming, S. (2002). Civilian American and European Surface Anthropometry Resource (CAESAR), Final Report. 1. Summary. Sytronics Inc. Dayton Oh.
• [33] Cao, D., Chen, C., Adjeroh, D., Ross, A. (2012). Predicting gender and weight from human metrology using a copula model. 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 162-169.
• [34] Kakadiaris, I.A., Sarafianos, N., Nikou, C. (2016). Show me your body: Gender classification from still images. 2016 IEEE International Conference on Image Processing (ICIP), 3156-3160.
• [35] Andersson, V.O., Amaral, L.S., Tonini, A.R., Araujo, R.M. (2015). Gender and Body Mass Index Classification Using a Microsoft Kinect Sensor. FLAIRS Conference, 103-106.
• [36] Loomis, A. (1943). Figure drawing for all it’s worth. Viking Pr.
• [37] Zhang, Z. (2012). Microsoft Kinect Sensor and Its Effect. IEEE MultiMedia, 19(2), 4-10.
• [38] Skeleton Position and Tracking State. https://msdn.microsoft.com/en-us/library/jj131025.aspx. (Jun. 2017)
• [39] Tsai, C.Y., Huang, C.H., Tsao, A.H. (2016). Graphics processing unit-accelerated multi-resolution exhaustive search algorithm for real-time keypoint descriptor matching in high-dimensional spaces. IET Comput. Vis., 10, 212-219.
• [40] Bui, L., Tran, D., Huang, X., Chetty, G. (2012). Face Gender Classification Based on Active Appearance Model And Fuzzy k-Nearest Neighbors. The 2012 International Conference on Image Processing, Computer Vision, and Pattern Recognition, CSREA Press, Las Vegas, USA, 17, 617-621,
• [41] Çamalan, S., Sengül G. (2016). Gender prediction by using Local Binary Pattern and K Nearest Neighbor and Discriminant Analysis classifications. 2016 24th Signal Processing and Communication Application Conference (SIU), 2161-2164.
• [42] Cortes, C., Vapnik, V. (1995). Support-vector networks. Mach. Learn., 20, 273-297.
• [43] Signoretto, M., Suykens, J.A.K. (2015). Kernel Methods. Kacprzyk J., Pedrycz W. (eds) Springer Handbook of Computational Intelligence. Springer, Berlin, Heidelberg.
• [44] Munsell, B.C., Temlyakov, A., Qu, C., Wang, S. (2012). Person identification using full-body motion and anthropometric biometrics from kinect videos. European Conference on Computer Vision, Springer, 91-100.
• [45] Gianaria, E., Grangetto, M., Lucenteforte, M., Balossino, N. (2014). Human classification using gait features. International Workshop on Biometric Authentication, Springer, 16-27.
• [46] Jaswante, A., Khan, A.U., Gour, B. (2014). Back Propagation Neural Network Based Gender Classification Technique Based on Facial Features. Int. J. Comput. Sci. Netw. Secur., 14, 91.
• [47] Rudra, S., Mitra, S., Das, S., Roy, A., Guha, S., Seal, D.B., Mukherjee, S., Chatterjee, S. (2016). Gender classification system from offline survey data using neural networks. Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), IEEE Annual, 1-5.
• [48] Hu, M., Zheng, Y., Ren, F., Jiang, H. (2014). Age estimation and gender classification of facial images based on Local Directional Pattern. Cloud Computing and Intelligence Systems (CCIS), IEEE 3rd International Conference, 103-107.
• [49] Basha, A.F., Jahangeer, G.S.B. (2012). Face gender image classification using various wavelet transform and support vector machine with various kernels. Int. J. Comput. Sci., 9, 150-157.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).