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Deepgender: real-time gender classification using deep learning for smartphones

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Abstract

Face recognition, expression identification, age determination, racial binding and gender classification are common examples of image processing computerization. Gender classification is very straightforward for us like we can tell by the person’s hair, nose, eyes, mouth and skin whether that person is a male or female with a relatively high degree of confidence and accuracy; however, can we program a computer to perform just as well at gender classification? The very problem is the main focus of this research. The conventional sequence for recent real-time facial image processing consists of five steps: face detection, noise removal, face alignment, feature representation and classification. With the aim of human gender classification, face alignment and feature vector extraction stages have been re-examined keeping in view the application of the system on smartphones. Face alignment has been made by spotting out 83 facial landmarks and 3-D facial model with the purpose of applying affine transformation. Furthermore, ‘feature representation’ is prepared through proposed modification in multilayer deep neural network, and hence we name it Deepgender. This convolutional deep neural network consists of some locally connected hidden layers without common weights of kernels as previously followed in legacy layered architecture. This specific case study involves deep learning as four convolutional layers, three max-pool layers (for downsizing of unrelated data), two fully connected layers (connection of outcome to all inputs) and a single layer of ‘multinomial logistic regression.’ Training has been made using CAS-PEAL and FEI which contain 99,594 face images of 1040 people and 2800 face images of 200 individuals, respectively. These images are either in different poses or taken under uncontrolled conditions which are close to real-time input facial image for gender classification application. The proposed system ‘Deepgender’ has registered 98% accuracy by combined use of both databases with the specific preprocess procedure, i.e., exhibiting alignment before resizing. Experiments suggest that accuracy is nearly 100% with frontal and nonblurred facial images. State-of-the-art steps have been taken to overcome memory and battery constraints in mobiles.

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References

  1. Abuarqoub, A., Hammoudeh, M., Alsboui, T.: An overview of information extraction from mobile wireless sensor networks. In: Andreev S., Balandin S., Koucheryavy Y. (eds.) Internet of Things, Smart Spaces, and Next Generation Networking, vol 7496, pp. 95–106. Springer, Berlin (2012)

  2. Khalid, S., Sajjad, S., Jabbar, S., Chang, H.: Accurate and efficient shape matching approach using vocabularies of multi-feature space representations. J. Real-Time Image Process. 1–17 (2015)

  3. Khalid, S., Arshad, S., Jabbar, S., Rho, S.: Robust framework to combine diverse classifiers assigning distributed confidence to individual classifiers at class level. Sci. World J. 2014, 492387 (2014). doi:10.1155/2014/492387

  4. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 2014. IEEE (2014)

  5. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS'12 Proceedings of the 25th International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc, USA (2012)

  6. Song, I., Kim, H.-J., and Jeon, P.B.: Deep learning for real-time robust facial expression recognition on a smartphone. In: IEEE International Conference on Consumer Electronics (ICCE), 2014. IEEE (2014)

  7. Meseguer, J.E., Calafate, C.T., Cano, J.C., Manzoni, P.: Drivingstyles: a smartphone application to assess driver behavior. In: IEEE Symposium on Computers and Communications (ISCC), 2013. IEEE (2013)

  8. Kim, Y.-D., Park, E., Yoo, S., Choi, T., Yang, L., Shin, D.: Compression of deep convolutional neural networks for fast and low power mobile applications. arXiv preprint arXiv:1511.06530, 2015

  9. Awais, M., Palmerini, L., Chiari, L.: Physical activity classification using body-worn inertial sensors in a multi-sensor setup. In: IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI), 2016. IEEE (2016)

  10. Del Rosario, M.B., Wang, K., Wang, J., Liu, Y., Brodie, M., Delbaere, K., Lovell, N.H., Lord, S.R., Redmond, S.J.: A comparison of activity classification in younger and older cohorts using a smartphone. Physiol. Meas. 35(11), 2269 (2014)

    Article  Google Scholar 

  11. Osadchy, M., Cun, Y.L., Miller, M.L.: Synergistic face detection and pose estimation with energy-based models. J. Mach. Learn. Res. 8(May), 1197–1215 (2007)

    Google Scholar 

  12. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)

  13. Huang, G.B.: Learning hierarchical representations for face verification with convolutional deep belief networks. In: CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2518–2525. IEEE Computer Society, Washington, DC (2012)

  14. Fang, Y., Wang, Z.: Improving LBP features for gender classification. In: International Conference on Wavelet Analysis and Pattern Recognition, 2008. ICWAPR '08. IEEE (2008)

  15. Zhang, N., Paluri, M., Ranzato, M.A., Darrell, T., Bourdev, L.: Panda: pose aligned networks for deep attribute modeling. (2014). https://arxiv.org/abs/1311.5591

  16. Hu, S.Y.D., Jou, B., Jaech, A., Savvides, M.: Fusion of region-based representations for gender identification. In: International Joint Conference on Biometrics (IJCB), 2011. IEEE (2011)

  17. Rai, P., Khanna, P.: A gender classification system robust to occlusion using Gabor features based (2D) 2 PCA. J. Vis. Commun. Image Represent. 25(5), 1118–1129 (2014)

    Article  Google Scholar 

  18. Fazl-Ersi, E., Mousa-Pasandi, M.E., Laganiere, R., Awad, M.: Age and gender recognition using informative features of various types. In: IEEE International Conference on Image Processing (ICIP), 2014. IEEE (2014)

  19. Leng, X., Wang, Y.: Improving generalization for gender classification. In: 15th IEEE International Conference on Image Processing, 2008. ICIP 2008. IEEE (2008)

  20. Chen, D.-Y., Lin, K.-Y.: Robust gender recognition for uncontrolled environment of real-life images. IEEE Trans. Consum. Electron. 56(3) (2010)

  21. Juefei-Xu, F., Verma, E., Goel, P., Cherodian, A., Savvides, M.: Deepgender: occlusion and low resolution robust facial gender classification via progressively trained convolutional neural networks with attention. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016. IEEE (2016)

  22. Biswas, S., Sil, J.: Gender recognition using fusion of spatial and temporal features. In: Kumar Kundu M., Mohapatra D., Konar A., Chakraborty A. (eds.) Advanced Computing, Networking and Informatics, vol. 1, pp. 109–116. Springer, Berlin (2014)

  23. Mansanet, J., Albiol, A., Paredes, R.: Local deep neural networks for gender recognition. Pattern Recogn. Lett. 70, 80–86 (2016)

    Article  Google Scholar 

  24. Zhang, K., Tan, L., Li, Z., Qiao, Y.: Gender and smile classification using deep convolutional neural networks. In: Book Gender and Smile Classification Using Deep Convolutional Neural Networks, pp. 34–38. (2016)

  25. Jiang, Y., Li, S., Liu, P., Dai, Q.: Multi-feature deep learning for face gender recognition. In: Book Multi-Feature Deep Learning for Face Gender Recognition, pp. 507–511. (2014)

  26. Barkan, O., Weill, J., Wolf, L., Aronowitz, H.: Fast high dimensional vector multiplication face recognition. In: Book Fast High Dimensional Vector Multiplication Face Recognition, pp. 1960–1967. (2013)

  27. Hassner, T.: Viewing real-world faces in 3D. In: Book Viewing Real-World Faces in 3D, pp. 3607–3614. (2013)

  28. Berg, T., Belhumeur, P.N.: Tom-vs-Pete classifiers and identity-preserving alignment for face verification. In: Book Tom-vs-Pete Classifiers and Identity-Preserving Alignment for Face Verification, pp. 7. (2012)

  29. Huang, G., Mattar, M., Lee, H., Learned-Miller, E.G.: Learning to align from scratch. In: Book Learning to Align from Scratch, pp. 764–772. (2012)

  30. Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Book Face Detection, Pose Estimation, and Landmark Localization in the Wild, pp. 2879–2886. (2012)

  31. Cao, X., Wei, Y., Wen, F., Sun, J.: Face alignment by explicit shape regression. Int. J. Comput. vis. 107(2), 177–190 (2014)

    Article  MathSciNet  Google Scholar 

  32. Chen, H., Li, J., Zhang, F., Li, Y., Wang, H.: 3D model-based continuous emotion recognition. In: Book 3D Model-Based Continuous Emotion Recognition, pp. 1836–1845. (2015)

  33. Levi, G., Hassner, T.: Age and gender classification using convolutional neural networks. In: Book Age and Gender Classification Using Convolutional Neural Networks, pp. 34–42. (2015)

  34. Hammoudeh, M., Newman, R.: Information extraction from sensor networks using the Watershed transform algorithm. Inf. Fusion 22, 39–49 (2015)

    Article  Google Scholar 

  35. Abuarqoub, A., Hammoudeh, M., Adebisi, B., Jabbar, S., Bounceur, A., Al-Bashar, H.: Dynamic clustering and management of mobile wireless sensor networks. Comput. Netw. 117, 62–75 (2017)

    Article  Google Scholar 

  36. Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 38(1), 149–161 (2008)

    Article  Google Scholar 

  37. Thomaz, C.E., Giraldi, G.A.: A new ranking method for principal components analysis and its application to face image analysis. Image Vis. Comput. 28(6), 902–913 (2010)

    Article  Google Scholar 

  38. Rahimi, M.R., Ren, J., Liu, C.H., Vasilakos, A.V., Venkatasubramanian, N.: Mobile cloud computing: a survey, state of art and future directions. Mobile Netw. Appl. 19(2), 133–143 (2014)

    Article  Google Scholar 

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Authors and Affiliations

  1. Government College University Faisalabad, Faisalabad, Pakistan

    Khurram Zeeshan Haider

  2. Department of Computer Science, COMSATS Institute of Information Technology, Sahiwal, Pakistan

    Kaleem Razzaq Malik

  3. Department of Computer Engineering, Bahria University, Islamabad, Pakistan

    Shehzad Khalid

  4. University of Engineering and Technology, Taxila, Pakistan

    Tabassam Nawaz

  5. Department of Computer Science, National Textile University, Faisalabad, Pakistan

    Sohail Jabbar

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  1. Khurram Zeeshan Haider
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  2. Kaleem Razzaq Malik
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  3. Shehzad Khalid
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  4. Tabassam Nawaz
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Correspondence to Kaleem Razzaq Malik.

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Haider, K.Z., Malik, K.R., Khalid, S. et al. Deepgender: real-time gender classification using deep learning for smartphones. J Real-Time Image Proc 16, 15–29 (2019). https://doi.org/10.1007/s11554-017-0714-3

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