Abstract
Issues related to realtime face recognition are perpetual even with many existing approaches. Generalizing these issues is tedious over different applications. In this paper, the real time issues such as tilt or rotation variation and few samples problem for face recognition are addressed and proposed an efficient method. In preprocessing, an edge detection method using Robert`s operator is utilized to identify facial borders for cropping purpose. The query images are axially tilted for different degrees of rotation. Both database and test images are segmented into one hundred fragments of 5 * 5 size each. Four different matrix characteristics are derived for each divided part of the image. Corresponding attributes are added to yield features related to final matrix. Final one hundred facial attributes are obtained by fusing diagonal features with one hundred features of matrix. Euclidean distance between the final attributes of gallery and query images is computed. The results on Yale dataset has superior performance compared to the existing different approaches and it is convincing over the dataset created.
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References
S. Evangelos, G. Hatice, C. Andrea, Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1113–1133 (2015)
K. Jyoti, R. Rajesha, K.M. Pooja, Facial expression recognition—a survey. In: 2nd International Symposium on Computer Vision and the Internet, Procedia Computer Science, vol. 58 (Elsevier, 2015), pp. 486–491
G. Castaneda, T.M. Khoshgoftaar, A survey of 2D face databases, in 16th IEEE International Conference on Information Reuse and Integration (2015), pp. 219–224
E. Gonzalez-Sosa, R. Vera-Rodriguez, J. Fierrez, P. Tome, J. Ortega-Garcia, Pose variability compensation using projective transformation for forensic face recognition, in IEEE International Conference of the Biometrics Special Interest Group (BIOSIG) (2015), pp. 1–5
Y. Gao, H.J. Lee, Cross-pose face recognition based on multiple virtual views and alignment error. Int. J. Pattern Recogn. Lett. 65, 170–176 (2015)
L. Shen, J. He, Face recognition with directional local binary patterns, in 6th Chinese Conference on Biometric Recognition, Lecture Notes in Computer Science, vol. 7098 (Springer, Berlin, 2011), pp. 10–16
S. Ding, H. Zhu, W. Jia, C. Su, A survey on feature extraction for pattern recognition. J. Artif. Intell. Rev. 37(3), 169–180 (2012)
X. Tan, S. Chen, Z.-H. Zhou, F. Zhang, Face recognition from a single image per person—a survey. J. Pattern Recogn. 39 (9), 1725–1745 (2006)
C. Wang, Y. Yang, Robust face recognition from single training image per person via auto-associative memory neural network, in IEEE International Conference on Electrical and Control Engineering (ICECE) (2011), pp. 4947–4950
S. Jingang, Q. Chun, From local geometry to global structure -learning latent subspace for low resolution face image recognition. IEEE Signal Process. Lett. 22(5), 554–558 (2015)
D. Changxing, X. Chang, T. Dacheng, Multi-task pose-invariant face recognition. IEEE Trans. Image Process. 24(3), 980–993 (2015)
http://vision.ucsd.edu/datasets/yale_face_dataset_original/yalefaces.zip
R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital image processing using MATLAB (Pearson Education India, 2004)
C. Ding, D. Zhou, X. He, H. Zha, R1-PCA-Rotational invariant L1-norm principal component analysis for robust subspace factorization, in 23rd International Conference on Machine Learning (Pittsburgh, 2006), pp. 1–8
L. Guangcan, L. Zhouchen, Y. Shuicheng, S. Ju, Y. Yong, Ma. Yi, Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2013)
M. Jaggi, M. Sulovsky, A simple algorithm for nuclear norm regularized problems, in 27th International Conference on Machine Learning, Israel, pp. 1–8 (2010)
Z. Ding, S. Suh, J.-J. Han, C. Choi, Y. Fu, Discriminative low-rank metric learning for face recognition, in 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), vol. 1 (2015), pp. 1–6
R. Shyam, Y.N. Singh, Face recognition using augmented local binary pattern and bray curtis dissimilarity metric, in 2nd IEEE International Conference on Signal Processing and Integrated Networks (2015),pp. 779−784
E. Zhang, Y. Li, F. Zhang, A single training sample face recognition algorithm based on sample extension, in 6th IEEE International Conference on Advanced Computational Intelligence (2013), pp. 324−327
S. Venkatramaphanikumar, V.K. Prasad, Gabor based face recognition with dynamic time warping, in 6th IEEE International Conference on Contemporary Computing (2013), pp. 349−353
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Jagadeesh, H.S., Babu, K.S., Raja, K.B. (2017). Recognizing Human Faces with Tilt. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-10-3156-4_44
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DOI: https://doi.org/10.1007/978-981-10-3156-4_44
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