This paper provides efficient and robust algorithms for real-time face detection and recognition in complex backgrounds. The algorithms are implemented using a series of signal processing methods including Ada Boost, cascade classifier, Local Binary Pattern (LBP), Haar-like feature, facial image pre-processing and Principal Component Analysis (PCA). The Ada Boost algorithm is implemented in a cascade classifier to train the face and eye detectors with robust detection accuracy. The LBP descriptor is utilized to extract facial features for fast face detection. The eye detection algorithm reduces the false face detection rate. The detected facial image is then processed to correct the orientation and increase the contrast, therefore, maintains high facial recognition accuracy. Finally, the PCA algorithm is used to recognize faces efficiently. Large databases with faces and non-faces images are used to train and validate face detection and facial recognition algorithms. The algorithms achieve an overall true-positive rate of 98.8% for face detection and 99.2% for correct facial recognition.
References
[1]
Zhu, X. and Ramanan, D. (2012) Face Detection, Pose Estimation and Landmark Localization in the Wild. IEEE Conference on Computer Vision and Pattern Recognition, Providence, 16-21 June 2012, 2879-2886.
[2]
Tsitsoulis, A. and Bourbakis, N.G. (2015) A Methodology for Extracting Standing Human Bodies From Single Images. IEEE Transactions on Human-Machine Systems, 45, 327-338. https://doi.org/10.1109/THMS.2015.2398582
[3]
Yanhun, Z. and Chongqing, L. (2003) Face Recognition Based on Support Vector Machine and Nearest Neighbor Classifier. Journal of Systems Engineering and Electronics, 14, 73-76.
[4]
Tayal, Y., Lamba, R. and Padhee, S. (2012) Automatic Face Detection Using Color Based Segmentation. International Journal of Scientific and Research Publications, 2, 1-7.
[5]
Tang, J., Deng, C., Huang, G.B. and Zhao, B. (2015) Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine. IEEE Transactions on Geoscience and Remote Sensing, 53, 1174-1185. https://doi.org/10.1109/TGRS.2014.2335751
[6]
Su, C.Y. and Yang, J.F. (2014) Histogram of Gradient Phases: A New Local Descriptor for Face Recognition. Computer Vision, 8, 556-567.
https://doi.org/10.1049/iet-cvi.2013.0208
[7]
Pavithra, R., Usha Ruby, A. and Chellin Chandran, J.G. (2014) Scale Invariant Feature Transform Based Face Recognition from a Single Sample per Person. International Journal of Computational Engineering Research, 4, 41-47.
[8]
Ahonen, T., Hadid, A. and Pietikainen, M. (2006) Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 2037-2041.
https://doi.org/10.1109/TPAMI.2006.244
[9]
Lienhart, R. and Maydt, J. (2002) An Extended Set of Haar-Like Features for Rapid Object Detection. 2002 International Conference on Image Processing, Vol. 1, Rochester, 22-25 September 2002, I-900-I-903.
https://doi.org/10.1109/icip.2002.1038171
[10]
Georgescu, D. (2011) A Real-Time Face Recognition System Using Eigenfaces. Journal of Mobile, Embedded and Distributed Systems, 3, 193-204.
[11]
Li, Z., Lin, D. and Tang, X. (2009) Nonparametric Discriminant Analysis for Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 755-761. https://doi.org/10.1109/TPAMI.2008.174
[12]
Ding, C., Xu, C. and Tao, D. (2015) Multi-Task Pose-Invariant Face Recognition. IEEE Transactions on Image Processing, 24, 980-993.
https://doi.org/10.1109/TIP.2015.2390959
[13]
Shen, C., Paisitkriangkrai, S. and Zhang, J. (2011) Efficiently Learning a Detection Cascade with Sparse Eigen-Vectors. IEEE Transactions on Image Processing, 20, 22-35. https://doi.org/10.1109/TIP.2010.2055880
[14]
Maturana, D., Mery, D. and Soto, A. (2009) Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes nearest Neighbor Classification. 2009 International Conference of the Chilean Computer Science Society, Santiago, 10-12 November 2009, 125-132. https://doi.org/10.1109/SCCC.2009.21
[15]
Mehmood, K. and Ahmad, B. (2013) Implementation of Face Detection System Using Adaptive Boosting Algorithm. International Journal of Computer Applications, 76, 51-57. https://doi.org/10.5120/13223-0639
[16]
Noh, S. (2012) χ2 Metric Learning for nearest Neighbor Classification and Its Analysis. 2012 21st International Conference on Pattern Recognition, Tsukuba, 11-15 November 2012, 991-995.
[17]
Pei, S.C. and Hsiao, Y.Z. (2015) Spatial Affine Transformations of Images by Using Fractional Shift Fourier Transform. 2015 IEEE International Symposium on Circuits and Systems, Lisbon, 24-27 May 2015, 1586-1589.
https://doi.org/10.1109/ISCAS.2015.7168951
[18]
Peddigari, V.R., Srinivasa, P. and Kumar, R. (2015) Enhanced ICA Based Face Recognition Using Histogram Equalization and Mirror Image Superposition. 2015 IEEE International Conference on Consumer Electronics, Las Vegas, 9-12 January 2015, 625-628. https://doi.org/10.1109/ICCE.2015.7066555
[19]
Reisert, M. and Burkhardt, H. (2008) Complex Derivative Filters. IEEE Transactions on Image Processing, 17, 2265-2274. https://doi.org/10.1109/TIP.2008.2006601
[20]
Center for Biological and Computational Learning at MIT and MIT. CBCL Face Database.
http://cbcl.mit.edu/projects/cbcl/software-datasets/FaceData1Readme.html
[21]
Chen, D., Ren, S., Wei, Y., Cao, X. and Sun, J. (2014) Joint Cascade Face Detection and Alignment. Computer Vision ECCV, Zurich, 6-12 September 2014, 109-122.
[22]
National Institute of Standards and Technology. NIST Mugshot Identification Database. http://www.nist.gov/srd/nistsd18.cfm
[23]
Mathias, M., Benenson, R., Pedersoli, M. and Van Gool, L. (2014) Face Detection without Bells and Whistles. Computer Vision ECCV, Zurich, 6-12 September 2014, Vol. 8692, 720-735.
[24]
FERET Program. The Facial Recognition Technology Database.
http://www.itl.nist.gov/iad/humanid/feret/
