A new way to age estimation for RGB-D images, based on a new face detection and extraction method for depth images

Автор: Seyed Muhammad Hossein Mousavi

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 11 vol.10, 2018 года.

Бесплатный доступ

With adding depth data to color data, it is possible to increase recognition accuracy significantly. Depth image mostly uses for calculating range or distance between object and sensor. Also they are used for making 3-D models of objects and increasing accuracy. Depending on the sensor’s depth quality, the recognition accuracy changes. Age estimation is useful for calculating the aging effects using prior patterns, which are recorded during years from subjects. In this paper, age estimation occurs using summation of RGB image edges gray value and summation of depth image’s entropy edges. Furthermore, a new face detection and extraction method for depth images is represented, which is based on standard deviation filter, ellipse fitting and some pre-post processing techniques. The advantage of this method is its speed and single image aspect capability. In this approach, there is no need to learning and classification process. Proposed method is between 10 to 20 times faster but lower accurate. System is validated with some benchmark color and color-depth (RGB-D) face databases, and in comparing with other age estimation methods, returned satisfactory and promising results. Because of the high speed in this method, it is possible to use it on real time applications. It is mentionable that this paper is the first age estimation research on RGB-D images.

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Depth image, Age estimation, Depth sensor, RGB-D image, Benchmark database, High speed

Короткий адрес: https://sciup.org/15016008

IDR: 15016008   |   DOI: 10.5815/ijigsp.2018.11.02

Список литературы A new way to age estimation for RGB-D images, based on a new face detection and extraction method for depth images

  • Geng, Xin, Zhi-Hua Zhou, and Kate Smith-Miles. "Automatic age estimation based on facial aging patterns." IEEE Transactions on pattern analysis and machine intelligence 29.12 (2007): 2234-2240.
  • Y. Fu, G. Guo, and T. Huang. Age synthesis and estimation via faces: a survey. IEEE Trans. Pattern Anal. Mach. Intell., 32(11):1955–1976, 2010.
  • Z. Song, B. Ni, D. Guo, T. Sim, and S. Yan. Learning universal multi-view age estimator by video contexts. In Proc. ICCV, 2011.
  • Gonzalez, Rafael (2008). 'Digital Image Processing, 3rd'. Pearson Hall. ISBN 9780131687288.
  • https://www.mathworks.com/discovery/pattern-recognition.html
  • Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons, 2012.
  • Geng, Xin, Zhi-Hua Zhou, and Kate Smith-Miles. "Automatic age estimation based on facial aging patterns." IEEE Transactions on pattern analysis and machine intelligence 28.12 (2007): 2234-2240.
  • The FG-NET Aging Database, http://sting.cycollege.ac.cy/~alanitis/ fgnetaging/index.htm, 2002.
  • https://www.slideshare.net/SugiuraTsukasa/kinect-v2-introduction-and-tutorial
  • Henry, Peter, et al. "RGB-D mapping: Using Kinect-style depth cameras for dense 3D modeling of indoor environments." The International Journal of Robotics Research 31.5 (2012): 647-663.
  • Ren, Zhou, Jingjing Meng, and Junsong Yuan. "Depth camera based hand gesture recognition and its applications in human-computer-interaction." Information, Communications and Signal Processing (ICICS) 2011 8th International Conference on. IEEE, 2011.
  • Cruz, Leandro, Djalma Lucio, and Luiz Velho. "Kinect and rgbd images: Challenges and applications." 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images Tutorials. IEEE, 2012.
  • El-laithy, Riyad A., Jidong Huang, and Michael Yeh. "Study on the use of Microsoft Kinect for robotics applications." Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION. IEEE, 2012.
  • Sun, Xudong, Pengcheng Wu, and Steven CH Hoi. "Face detection using deep learning: An improved faster rcnn approach." arXiv preprint arXiv:1701.08289 (2017).
  • ter Haar, Frank B., and Remco C. Veltkamp. "3D face model fitting for recognition." European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2008.
  • Geng, Xin, Zhi-Hua Zhou, and Kate Smith-Miles. "Automatic age estimation based on facial aging patterns." IEEE Transactions on pattern analysis and machine intelligence 29.12 (2007): 2234-2240.
  • Zhuang, Xiaodan, et al. "Face age estimation using patch-based hidden markov model supervectors." Pattern Recognition, 2008. ICPR 2008. 19th International Conference on. IEEE, 2008.
  • Ricanek, Karl, et al. "Generalized multi-ethnic face age-estimation." Biometrics: Theory, Applications, and Systems, 2009. BTAS'09. IEEE 3rd International Conference on. IEEE, 2009.
  • Li, Changsheng, et al. "Learning ordinal discriminative features for age estimation." Computer vision and pattern recognition (cvpr), 2012 ieee conference on. IEEE, 2012.
  • Han, Hu, Charles Otto, and Anil K. Jain. "Age estimation from face images: Human vs. machine performance." Biometrics (ICB), 2013 International Conference on. IEEE, 2013.
  • Yi, Dong, Zhen Lei, and Stan Z. Li. "Age estimation by multi-scale convolutional network." Asian Conference on Computer Vision. Springer, Cham, 2014.
  • Levi, Gil, and Tal Hassner. "Age and gender classification using convolutional neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2015.
  • Niu, Zhenxing, et al. "Ordinal regression with multiple output cnn for age estimation." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
  • Liu, Hao, et al. "Group-aware deep feature learning for facial age estimation." Pattern Recognition 66 (2017): 82-94.
  • Viola, Paul, and Michael Jones. "Rapid object detection using a boosted cascade of simple features." Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Vol. 1. IEEE, 2001.
  • Carcagnì, Pierluigi, et al. "Facial expression recognition and histograms of oriented gradients: a comprehensive study." SpringerPlus 4.1 (2015): 645.
  • ter Haar, Frank B., and Remco C. Veltkamp. "3D face model fitting for recognition." European Conference on Computer Vision. Springer, Berlin, Heidelberg, 2008.
  • Chang, Dah-Chung, and Wen-Rong Wu. "Image contrast enhancement based on a histogram transformation of local standard deviation." IEEE transactions on medical imaging 17.4 (1998): 518-531.
  • Gander, Walter, Gene H. Golub, and Rolf Strebel. "Least-squares fitting of circles and ellipses." BIT Numerical Mathematics 34.4 (1994): 558-578.
  • http://www.astro.cornell.edu/research/projects/compression/entropy.html
  • Willmott, Cort J.; Matsuura, Kenji (December 19, 2005). "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance". Climate Research. 30: 79–82.
  • Li, Billy YL, et al. "Using kinect for face recognition under varying poses, expressions, illumination and disguise." Applications of Computer Vision (WACV), 2013 IEEE Workshop on. IEEE, 2013.
  • Szwoch, Mariusz. "FEEDB: a multimodal database of facial expressions and emotions." Human System Interaction (HSI), 2013 The 6th International Conference on. IEEE, 2013.
  • Hyndman, R. and Koehler A. (2005). "Another look at measures of forecast accuracy"
  • Geng, Xin, et al. "Learning from facial aging patterns for automatic age estimation." Proceedings of the 14th ACM international conference on Multimedia. ACM, 2006.
  • Hayashi, Jun-ichiro, et al. "Age and gender estimation from facial image processing." SICE 2002. Proceedings of the 41st SICE Annual Conference. Vol. 1. IEEE, 2002.
  • Guo, Guodong, et al. "Image-based human age estimation by manifold learning and locally adjusted robust regression." IEEE Transactions on Image Processing 17.7 (2008): 1178-1188.
  • Lanitis, Andreas, Chrisina Draganova, and Chris Christodoulou. "Comparing different classifiers for automatic age estimation." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 34.1 (2004): 621-628.
  • Guo, Guodong, et al. "Human age estimation using bio-inspired features." Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.
  • Yang, Guangzheng, and Thomas S. Huang. "Human face detection in a complex background." Pattern recognition 27.1 (1994): 53-63.
  • Wang, Jianguo, and Tieniu Tan. "A new face detection method based on shape information." Pattern Recognition Letters 21.6-7 (2000): 463-471.
  • Wu, Haiyuan, Qian Chen, and Masahiko Yachida. "Face detection from color images using a fuzzy pattern matching method." IEEE Transactions on Pattern Analysis & Machine Intelligence 6 (1999): 557-563.
  • Hjelmås, Erik, and Boon Kee Low. "Face detection: A survey." Computer vision and image understanding 83.3 (2001): 236-274.
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