The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition
Abstract
A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate.
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Cites
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,707
Marr, D., & Hildreth, E. (1980) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1980.0020 · Cited by 6,510
Face recognition: A literature survey
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003) · ACM Computing Surveys · doi:10.1145/954339.954342 · Cited by 6,163
Swain, M. J., & Ballard, D. H. (1991) · International Journal of Computer Vision · doi:10.1007/bf00130487 · Cited by 5,623
Face recognition using eigenfaces
Turk, M. A., & Pentland, A. P. (2002) · Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1991.139758 · Cited by 5,197
Габор, Д. (1946) · Journal of the IEE · Cited by 5,036
The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
High confidence visual recognition of persons by a test of statistical independence
Daugman, J. G. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.244676 · Cited by 3,252
Face recognition by elastic bunch graph matching
Wiskott, L., Fellous, J.-M., Kuiger, N., & von der Malsburg, C. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598235 · Cited by 2,883
Human and machine recognition of faces: a survey
Chellappa, R., Wilson, C. L., & Sirohey, S. (1995) · Proceedings of the IEEE · doi:10.1109/5.381842 · Cited by 2,494
Face Recognition with Local Binary Patterns
Ahonen, T., Hadid, A., & Pietikäinen, M. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24670-1_36 · Cited by 2,462
Liu, C., & Wechsler, H. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.999679 · Cited by 1,886
Distortion invariant object recognition in the dynamic link architecture
Lades, M., Vorbruggen, J. C., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R. P., & Konen, W. (1993) · IEEE Transactions on Computers · doi:10.1109/12.210173 · Cited by 1,805
Face Recognition by Elastic Bunch Graph Matching*†
Wiskott, L., Fellous, J.-M., Krüger, N., & von der Malsburg, C. (2022) · INTELLIGENT BIOMETRIC TECHNIQUES in FINGERPRINT and FACE RECOGNITION · doi:10.1201/9780203750520-11 · Cited by 1,781
A Survey of Computer Vision-Based Human Motion Capture
Moeslund, T. B., & Granum, E. (2001) · Computer Vision and Image Understanding · doi:10.1006/cviu.2000.0897 · Cited by 1,768
Online palmprint identification
Zhang, D., Kong, W.-K., You, J., & Wong, M. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1227981 · Cited by 1,506
Probabilistic visual learning for object representation
Moghaddam, B., & Pentland, A. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598227 · Cited by 1,441
Local grayvalue invariants for image retrieval
Schmid, C., & Mohr, R. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.589215 · Cited by 1,432
Zhang, W., Shan, S., Gao, W., Chen, X., & Zhang, H. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.147 · Cited by 1,024
Self-citationThe Resonant Retina: Exploiting Vibration Noise to Optimally Detect Edges in an Image
Zhang, D. D., Kong, W.-K., You, J., & Wong, M. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · Cited by 901
Local feature analysis: a general statistical theory for object representation
Penev, P. S., & Atick, J. J. (1996) · Network Computation in Neural Systems · doi:10.1088/0954-898x/7/3/002 · Cited by 627
Human Detection Based on a Probabilistic Assembly of Robust Part Detectors
Mikolajczyk, K., Schmid, C., & Zisserman, A. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24670-1_6 · Cited by 608
Gabor filter-based edge detection
Mehrotra, R., Namuduri, K. R., & Ranganathan, N. (1992) · Pattern Recognition · doi:10.1016/0031-3203(92)90121-x · Cited by 444
Automatic Detection and Recognition of Signs From Natural Scenes
Chen, X., Yang, J., Zhang, J., & Waibel, A. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819223 · Cited by 279
Self-citationRobust Recognition Using Eigenimages
Leonardis, A., & Bischof, H. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0830 · Cited by 262
Multiresolution histograms and their use for recognition
Hadjidemetriou, E., Grossberg, M. D., & Nayar, S. K. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.32 · Cited by 253
Color indexing with weak spatial constraints:
Stricker, M. A., & Dimai, A. (1996) · SPIE Proceedings · doi:10.1117/12.234802 · Cited by 232
Fingerprint feature extraction using Gabor filters
Lee, & Wang (1999) · Electronics Letters · doi:10.1049/el:19990213 · Cited by 173
A Bayesian similarity measure for direct image matching
Moghaddam, B., Nastar, C., & Pentland, A. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546848 · Cited by 154
Wavelet transforms and neural networks for compression and recognition
Szu, H. H., Telfer, B. A., & Garcia, J. P. (1996) · Neural Networks · doi:10.1016/0893-6080(95)00051-8 · Cited by 87
AdaBoost Gabor Fisher Classifier for Face Recognition
Shan, S., Yang, P., Chen, X., & Gao, W. (2005) · Lecture notes in computer science · doi:10.1007/11564386_22 · Cited by 64
Self-citationRetrieving images by appearance
Ravela, S., & Manmatha, R. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1998.710780 · Cited by 32
Learning informative features for spatial histogram-based object detection
Zhang, H., Gao, W., Chen, X., & Zhao, D. (2005) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2005.1556154 · Cited by 22
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Luan, S., Chen, C., Zhang, B., Han, J., & Liu, J. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2835143 · Cited by 428
Self-citationFusing local patterns of Gabor magnitude and phase for face recognition
Xie, S., Shan, S., Chen, X., & Chen, J. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2041397 · Cited by 395
Self-citationLocal binary features for texture classification: Taxonomy and experimental study
Liu, L., Fieguth, P., Guo, Y., Wang, X., & Pietikäinen, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.032 · Cited by 369
Metadata
- Authors
- Zhang, B., Shan, S., Chen, X., & Gao, W.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2007-01
- Volume
- 16
- Issue
- 1
- Pages
- 57-68
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2054891869
- Cited by
- 580
- References
- 34
- FWCI
- 22.35
- Type
- journal article
- Language
- en
- Open access
- closed
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