• Laser & Optoelectronics Progress
  • Vol. 56, Issue 23, 231002 (2019)
Xiaojia Jiang and Shuhui Gao*
Author Affiliations
  • Institute of Forensic Science, People's Public Security University of China, Beijing 102623, China
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    DOI: 10.3788/LOP56.231002 Cite this Article Set citation alerts
    Xiaojia Jiang, Shuhui Gao. Automatic Classification of Microscopic Hair Images Based on Improved Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231002 Copy Citation Text show less
    References

    [1] Zhang W, Xu Y C. A review and prospects of the research on hair microstructure[J]. Acta Theriologica Sinica, 23, 339-345(2003).

    [2] Gan Y L, Guo Z W, Liu M H et al. Scanning electron microscopy study of animal hair in criminal cases[J]. Journal of Chinese Electron Microscopy Society, 22, 489(2003).

    [3] Zou Y, Quan Y K, Zhu Y C et al. A study on the microtopography of gunshot damaged hair using ESEM[J]. Chinese Journal of Forensic Medicine, 21, 325-327(2006).

    [4] Zou Y, Tao K M, Li L X et al[J]. A study on the morphological characterizations of hair physical damage using ESEM Forensic Science and Technology, 2006, 4-6.

    [5] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2818-2826(2016).

    [6] Zhou F Y, Jin L P, Dong J. Review of convolutional neural network[J]. Chinese Journal of Computers, 40, 1229-1251(2017).

    [7] Li Y D, Hao Z B, Lei H. Survey of convolutional neural network[J]. Journal of Computer Applications, 36, 2508-2515, 2565(2016).

    [8] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 313, 504-507(2006).

    [9] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 770-778(2016).

    [10] Chen X, Zhu R, Wang Z Y. Handwritten digits recognition based on fused convolutional neural network model[J]. Computer Engineering, 43, 187-192(2017).

    [11] Zhong J Y, Yang B, Li Y H et al. Image fusion and super-resolution with convolutional neural network[M]. ∥Tan T, Li X, Chen X, et al. Pattern recognition. CCPR 2016. Communications in computer and information science. Singapore: Springer, 663, 78-88(2016).

    [12] Xiao J S, Liu E Y, Zhu L et al. Improved image super-resolution algorithm based on convolutional neural network[J]. Acta Optica Sinica, 37, 0318011(2017).

    [13] Dong J F, Zheng B C, Yang Z J. Character recognition of license plate based on convolution neural network[J]. Journal of Computer Applications, 37, 2014-2018(2017).

    [14] Nguyen N G, Tran V A, Ngo D L et al. DNA sequence classification by convolutional neural network[J]. Journal of Biomedical Science and Engineering, 9, 280-286(2016).

    [15] Jiang S. Image recognition based on convolutional neural networks[D]. Changchun: Jilin University(2017).

    [16] Gao H L. Military image classification based on convolutional neural network[J]. Application Research of Computers, 34, 3518-3520(2017).

    [17] Wang X, Liu Y, Li G Y. Moving object detection algorithm based on improved visual background extractor algorithm[J]. Laser & Optoelectronics Progress, 56, 011007(2019).

    [18] Schroff F, Kalenichenko D, Philbin J. FaceNet: a unified embedding for face recognition and clustering. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 815-823(2015).

    [19] Sun Y, Liang D, Wang X G et al. -02-03)[2019-04-28]. https:∥arxiv., org/abs/1502, 00873(2015).

    [20] Hafemann L G, Sabourin R, Oliveira L S. Learning features for offline handwritten signature verification using deep convolutional neural networks[J]. Pattern Recognition, 70, 163-176(2017).

    [21] Liang X L. Verification of off-line handwritten signature based on the improved neural network[D]. Beijing: China University of Political Science and Law(2017).

    [22] Abdel-Hamid O, Mohamed A R, Jiang H et al. Convolutional neural networks for speech recognition[J]. ACM Transactions on Audio, Speech, and Language Processing, 22, 1533-1545(2014).

    [23] Karpathy A, Toderici G, Shetty S et al. Large-scale video classification with convolutional neural networks. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 1725-1732(2014).

    [24] Li Y. Research on gait recognition based on three-dimensional convolutional neural network[D]. Beijing: Beijing Jiaotong University(2018).

    [25] Zhao Y J, Zhou S P. Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network[J]. Sensors, 17, 478(2017).

    [26] Wen Y D, Zhang K P, Li Z F et al. A discriminative feature learning approach for deep face recognition[M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9911, 499-515(2016).

    [27] Yu C B, Tian T, Xiong D E et al. Joint supervision of center loss and Softmax loss for face recognition[J]. Journal of Chongqing University, 41, 92-100(2018).

    Xiaojia Jiang, Shuhui Gao. Automatic Classification of Microscopic Hair Images Based on Improved Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231002
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