INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.11, No.3, Mar. 2019

An Efficient Scheme of Deep Convolution Neural Network for Multi View Face Detection

Full Text (PDF, 828KB), PP.53-61


Views:2   Downloads:0

Author(s)

Shivkaran Ravidas, M.A. Ansari

Index Terms

Face detection;multi view face detection;deep learning;convolutional neural network (CNN) and Computer vision

Abstract

The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Multi-view face detection is a challenging issue due to wide changes in appearance under different pose expression and illumination conditions. To address challenges, we designed a deep learning scheme with different network structures to enhance the multi view faces. More specifically, we design cascade architecture on convolutional neural networks (CNNs) which quickly reject non-face regions. Implementation, detection and retrieval of faces will be obtained with the help of direct visual matching technology. Further, a probabilistic calculation of resemblance among the images of face will be conducted on the basis of the Bayesian analysis for achieving detection of various faces.  Experiment detects faces with ±90 degree out of plane rotations. Fine-tuned AlexNet is used to detect multi view faces. For this work, we extracted examples of training from AFLW (Annotated Facial Landmarks in the Wild) dataset that involve 21K images with 24K annotations of the face.

Cite This Paper

Shivkaran Ravidas, M.A. Ansari, "An Efficient Scheme of Deep Convolution Neural Network for Multi View Face Detection", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.3, pp.53-61, 2019. DOI: 10.5815/ijisa.2019.03.06

Reference

[1]YI, Fang, L. I. Hao, and J. I. N. Xiaojie, “Improved Classification Methods for Brain Computer Interface System”, International Journal Computer Network and Information Security, ISSN: 2074-9104, Vol. 4, Issue: 2, pp.15-21, Mar-2012.

[2]Liu, Chengjun, and Harry Wechsler, “Gabor Feature Based Classification Using the Enhanced Fisher Linear Discriminant Model for Face Recognition”, IEEE Transactions on Image processing, ISSN: 1057-7149, Vol. 11, Issue:4, pp. 467-476, Apr-2002

[3]Ahonen, T., Hadid, A. and Pietikainen, M., “Face Description with Local Binary Patterns: Application to Face Recognition”, IEEE Transactions on Pattern Analysis & Machine Intelligence, ISSN: 0162-8828, Vol. 12, Issue: 12, pp. 2037-2041, Dec-2006

[4]Lowe, David G., “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of computer Vision, ISSN: 1573-1405, Vol. 60, Issue:  0.2, pp.91-110, Nov-2004.

[5]Simonyan, K., Parkhi, O. M., Vedaldi, A., & Zisserman, A., “Fisher Vector Faces in the Wild”, In British machine Vision Conference (BMVC), ISBN: 1-901725-49-9, Vol. 2, No. 3, pp. 4, Sep-2013.

[6]Simonyan, Karen, Andrea Vedaldi, and Andrew Zisserman, “Learning Local Feature Descriptors using Convex Optimisation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828, Vol. 36, Issue: 8, pp.1573-1585, Aug-2014

[7]Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D. and Zitnick, C. L., “Microsoft coco: Common Objects in Context”, European conference on Computer Vision, ISBN: 978-3-319-10602-1, Springer, Cham, pp. 740-755, Sep- 2014.

[8]Li, Haoxiang, Zhe Lin, Jonathan Brandt, Xiaohui Shen, and Gang Hua, “Efficient Boosted Exemplar-Based Face Detection”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, ISSN: 1063-6919, pp. 1843-1850, 2014.

[9]Matsumoto Yoshio and Alexander Zelinsky, “An Algorithm for Real-Time Stereo Vision Implementation of Head Pose and Gaze Direction Measurement”, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, ISBN: 0-7695-0580-5, pp. 499, Mar-2000.

[10]Ansari, M. A., and Aishwarya Agnihotri, “An Efficient Face Recognition System Based on PCA and Extended Biogeography-Based Optimization Technique”, Indian Journal of Industrial and Applied Mathematics, ISSN: 1945-919X, Vol. 7, Isuue: 2, pp. 285-305, 2016.

[11]Jaiswal, Sushma, “Comparison Between Face Recognition Algorithm-Eigenfaces, Fisherfaces and Elastic Bunch Graph Matching”, Journal of Global Research in Computer Science, ISSN: 2229-371X, Vol. 2, Issue: 7, pp.187-193, Aug-2011.

[12]R. Girshick, J. Donahue, T. Darrell, and J. Malik., “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, ISBN: 978-1-5386-0733-6, pp. 580-587, 2017.

[13]W. Zhang, G. Zelinsky, and D. Samaras, “Real-time Accurate Object Detection using Multiple Resolutions”, Proceeding IEEE International Conference on Computer Vision, ISBN: 9781424416301, pp. 1-8, Oct-2007.

[14]D. Park, D. Ramanan, and C. Fowlkes, “Multiresolution Models for Object Detection”, European Conference on Computer Vision, ISSN: 1611-3349 Springer, pp. 241-254, Sep-2010.

[15]Vaillant, Régis, Christophe Monrocq, and Yann Le Cun, “Original Approach for the Localisation of Objects in Images”, IEE Proceedings Vision, Image and Signal Processing, ISSN: 1359-7108, Vol. 141, Issue: 4, pp.245-250, Aug-1994.

[16]Rowley, Henry A., Shumeet Baluja, and Takeo Kanade, “Neural Network-Based Face Detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828, Vol. 20, Issue: 1, pp.23-38, Jan-1998.

[17]Yang, Ming-Hsuan, David J. Kriegman, and Narendra Ahuja, “Detecting Faces in Images: A Survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN: 0162-8828, Vol. 24, Issue:1, pp. 34-58, Jan -2002

[18]Mahmoodi, Mohammad Reza, and Sayed Masoud Sayedi, “A Comprehensive Survey on Human Skin Detection”, International Journal of Image Graphics and Signal Processing, ISSN: 2074-9082, Vol.  8, issue: 5, pp. 1-35, May-2016.

[19]Rowley, H., Baluja, S. and Kanade, T., “Rotation Invariant Neural Network-Based Face Detection”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, ISSN: 1063-6919, 1998, pp. 38-44, Jun-1998.

[20]Zhang, C. and Zhang, Z., “Improving Multi-View Face Detection with Multi-Task Deep Convolutional Neural Network”, Applications of Computer Vision (WACV), IEEE Winter Conference, ISBN: 9781479949847 pp.1036-1041, Mar-2014.

[21]Farfade, S.S., Saberian, M.J., and Li, L.J.,“Multi-view Face Detection using Deep Convolutional Neural Networks”, Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, ISBN: 978-1-4503-3274-3 ACM, pp. 643-650, Jun- 2015.

[22]Parkhi, O.M., Vedaldi, A. and Zisserman, A., “Deep Face Recognition”, British Machine Vision Conference (BMVC), ISBN: 1-901725-53-7, Vol. 1, Issue: 3, p.6, Sep-2015.

[23]Li. Haoxiang, Zhe Lin, Xiaohui Shen, Jonathan Brandt, and Gang Hua, “A Convolutional Neural Network Cascade for Face Detection”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, ISBN: 9781467369657, pp. 5325-5334, 2015.

[24]Zhu, Zhenyao, Ping Luo, Xiaogang Wang, and Xiaoou Tang, “Multi-view Perceptron: a Deep Model for Learning Face Identity and View Representations”, Advances in Neural Information Processing Systems, ISBN: 978-1-5108-0041-0, pp.217-225, 2014.

[25]Rath, Subrat Kumar, and Siddharth Swarup Rautaray, “A Survey on Face Detection and Recognition Techniques in Different Application Domain”, International Journal of Modern Education and Computer Science”, ISSN: 2075-017X, Vol. 6, Issue: 8, pp.34, 2014

[26]Hjelmås, Erik, and Boon Kee Low, “Face Detection: A Survey”, Computer Vision and Image Understanding, ISSN: 1077-3142, Vol. 83, Issue: 3, pp.236-274, Sep- 2001.

[27]Sheikh Amanur Rahman, M.A. Ansari and Santosh Kumar Upadhyay, “An Efficient Architecture for Face Detection in Complex Images”, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277-128X, Vol. 2, Issue 12, pp. 211-216, Dec-2012.

[28]Mariappan, M., Fang, T.W., Nadarajan, M. and Parimon, N., “Face Detection and Auto Positioning for Robotic Vision System”, International Journal of Image, Graphics and Signal Processing, ISSN: 2074-9082, Vol. 7, Issue: 12, pp. 1-9, Nov-2015.

[29]Anam, R., Rahman, M., Haque, M.O. and Islam, M.S., “Computer Vision Based Automation System for Detecting Objects”, International Journal of Intelligent Systems and Applications, ISSN: 2074-9058, Vol. 7, Issue: 12, p.65, Nov- 2015

[30]Sharma, Kartikeya, Shivkaran Ravidas, and M. A. Ansari, “A Novel Technique for Face Alignment Using Deep Convolutional Neural Networks”, Indian Journal of Industrial and Applied Mathematics, ISSN: 1945-919X, Vol. 8, Issue: 1, pp.107-117, 2017.

[31]Yang, Bin, Junjie Yan, Zhen Lei, and Stan Z. Li, “Aggregate Channel Features for Multi-view Face Detection”, International Joint Conference on Biometrics (IJCB), In Proceeding on IEEE International Joint Conference, ISBN: 9781479935857, pp. 1-8, Sep-2014.

[32]Jyoti S. Bedre ,Shubhangi Sapkal, “Comparative Study of Face Recognition Techniques: A Review”, Proceeding Published in International Journal of Computer Applications, ISSN: 0975 - 8887, Vol. 12,  2012

[33]Sun Y, Chen Y, Wang X, Tang X., “Deep Learning Face Representation by Joint Identification-verification”, Advances in Neural Information Processing Systems, ISBN: 9781510800410, pp. 1988-1996, Dec-2014

[34]Sun, Yi, Xiaogang Wang, and Xiaoou Tang, “Deep Learning Face Representation from Predicting 10,000 Classes”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, ISSN: 1063-6919, pp. 1891-1898, 2014.

[35]Jain. Vidit and Erik Learned-Miller,” Fddb: A Benchmark for Face Detection in Unconstrained Setting”, Technical Report UM-CS-2010-009, Vol.88, University of Massachusetts, Amherst, 2010.

[36]Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton., “ImageNet Classification with Deep Convolutional Neural Networks”, Advances in Neural Information Processing Systems, ISBN: 9781627480031, pp. 1097-1105, 2012.