Abstract
Human face is a widely used biometric modality for verification and revealing the identity of a person. In spite of a great deal of research on face recognition, it still is a challenging issue. Recently, the outstanding performance of deep learning has attracted a good deal of research interest for face recognition. In comparison with hand-engineered features, learning-based face features have proven their superiority in encoding discriminative information. Inspired by deep learning, we introduce a simple and efficient unsupervised feature learning scheme for face recognition. This scheme employs principle component analysis (PCA), local binary pattern (LBP), and pyramid pooling. Following the architecture of a convolutional neural network, the proposed scheme contains three types of layers: convolutional, nonlinear, and pooling layers. PCA is used to learn a filter bank for the convolutional layer. This is followed by LBP operator that encodes the local texture and adds nonlinearity to the feature maps of convolutional layer, which are then pooled using spatial pyramid pooling. To corroborate the effectiveness of the scheme (which we call as PCAPool), extensive experiments were performed on challenging benchmark databases: FERET, Yale, Extended Yale B, AR, and multi-PIE. The comparison reveals that PCAPool performs better than the state-of-the-art methods.
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References
Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720
Turk MA, Pentland AP (1991) Face recognition using eigenfaces. In IEEE Computer Society conference on computer vision and pattern recognition, 1991. Proceedings CVPR’91, pp 586–591
Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process Publ IEEE Signal Process Soc 11(4):467–476
Lei Z, Li SZ, Chu R, Zhu X (2007) Face recognition with local gabor textons. In: Lee S-W, Li SZ (eds) Advances in biometrics: international conference, ICB 2007, Seoul, Korea, August 27–29, 2007. Proceedings. Springer, Berlin, pp 49–57
Ahonen T, Hadid A, Pietikäinen M (2006) Face description with local binary patterns: application to face recognition. IEEE Trans Pattern Anal Mach Intell 28(12):2037–2041
Wright J, Yang A, Ganesh A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227
Wright J, Ma Y, Mairal J, Sapiro G, Huang TS, Yan S (2010) Sparse representation for computer vision and pattern recognition. Proc IEEE 98(6):1031–1044
Deng W, Hu J, Guo J (2013) In defense of sparsity based face recognition. In: 2013 IEEE conference on computer vision and pattern recognition (CVPR), pp 399–406
Yang M, Zhang L, Shiu SCK, Zhang D (2013) Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary. Pattern Recognit 46(7):1865–1878
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2169–2178
Yang J, Yu K, Gong Y, Huang T (2009) Linear spatial pyramid matching using sparse coding for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1794–1801
Shen F, Shen C, Zhou X, Yang Y, Shen HT (2016) Face image classification by pooling raw features. Pattern Recognit 54:94–103
Jarrett K, Kavukcuoglu K, Ranzato M, LeCun Y (2009) What is the best multi-stage architecture for object recognition? In: 2009 IEEE 12th international conference on computer vision, pp 2146–2153
Kavukcuoglu K, Sermanet P, Boureau Y, Gregor K, Mathieu M, Cun YL (2010) Learning convolutional feature hierarchies for visual recognition. Presented at the Advances in Neural Information Processing Systems, 2010, pp 1090–1098
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision—ECCV 2014. Springer, Berlin, pp 818–833
Zhu Z, Luo P, Wang X, Tang X (2013) Deep learning identity-preserving face space. In: Proceedings of the 2013 IEEE international conference on computer vision, Washington, DC, USA, pp 113–120
Sun Y, Wang X, Tang X (2014) Deep learning face representation from predicting 10,000 classes. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition, Washington, DC, USA, pp 1891–1898
Chan TH, Jia K, Gao S, Lu J, Zeng Z, Ma Y (2015) PCANet: a simple deep learning baseline for image classification? IEEE Trans Image Process 24(12):5017–5032
Zhang G, Huang X, Li SZ, Wang Y, Wu X (2004) Boosting local binary pattern (LBP)-based face recognition. In: Li SZ, Lai J, Tan T, Feng G, Wang Y (eds) Advances in biometric person authentication. Springer, Berlin, pp 179–186
Jabid T, Kabir MH, Chae O (2010) Gender classification using local directional pattern (LDP). In: Proceedings of the 2010 20th international conference on pattern recognition, Washington, DC, USA, pp 2162–2165
Gong Y, Lazebnik S (2011) Comparing data-dependent and data-independent embeddings for classification and ranking of internet images. In: Proceedings of the 2011 IEEE conference on computer vision and pattern recognition, Washington, DC, USA, pp 2633–2640
Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The FERET database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306
Martinez A, Benavente R. The AR face database. CVC, Technical Report
Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE TPAMI 23(6):643–660
Gross R, Matthews I, Cohn J, Kanade T, Baker S (2008) Multi-PIE. In: 8th IEEE international conference on automatic face gesture recognition, 2008. FG’08, pp 1–8
Zhang B, Shan S, Chen X, Gao W (2007) Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Trans. Image Process. 16(1):57–68
Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3360–3367
Vu N-S, Caplier A (2012) Enhanced patterns of oriented edge magnitudes for face recognition and image matching. IEEE TIP 21(3):1352–1368
Lu J, Tan Y-P, Wang G (2013) Discriminative multi-manifold analysis for face recognition from a single training sample per person. IEEE TPAMI 35(1):39–51
Tan X, Triggs B (2007) Fusing Gabor and LBP feature sets for kernel-based face recognition. In: International workshop on analysis and modeling of faces and gestures, pp 235–249
Shi Q, Eriksson A, vanden Hengel A, Shen C (2011) Is face recognition really a compressive sensing problem? In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 553–560
Kumar R, Banerjee A, Vemuri BC, Pfister H (2012) Trainable convolution filters and their application to face recognition. IEEE Trans Pattern Anal Mach Intell 34(7):1423–1436
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
He X, Yan S, Hu Y, Niyogi P, Zhang H-J (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340
Cai D, He X, Han J, Zhang H-J (2006) Orthogonal Laplacian faces for face recognition. IEEE Trans Image Process 15(11):3608–3614
Tan X, Triggs B (2010) Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE TIP 19(6):1635–1650
Yang M, Zhang L, Yang J, Zhang (2011) Robust sparse coding for face recognition. In: Proceedings of IEEE international conference on computer vision and pattern recognition, pp 625–632
Tao Q-C, Liu Z-M, Bebis G, Hussain M (2015) Face recognition using a novel image representation scheme and multi-scale local features. Int J Biom 7(3):191–212
Ojala T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987
Acknowledgements
The authors are thankful to the Deanship of Scientific Research, King Saud University, Riyadh, Saudi Arabia for funding this work through the Research Group No. RGP-1439-067.
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Alahmadi, A., Hussain, M., Aboalsamh, H.A. et al. PCAPooL: unsupervised feature learning for face recognition using PCA, LBP, and pyramid pooling. Pattern Anal Applic 23, 673–682 (2020). https://doi.org/10.1007/s10044-019-00818-y
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DOI: https://doi.org/10.1007/s10044-019-00818-y