双模态人脸识别系统的研究与实现
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摘要
在近二十年中自动人脸识别引起了人们的广泛关注,人们迫切希望计算机能拥有和人一样的强大的依据人脸来识别人身份的能力从而使世界变的更加智能。但是,到目前为止,自动人脸识别还远未达到人们的期望;尤其是在复杂情况下,传统的基于人脸图像间相似性计算的自动人脸识别技术的性能难以达到应用要求。例如,脸部存在部分遮挡且遮挡比例稍大的情况下,自动人脸识别技术一般会出现识别错误或拒识。又如,研究表明,在光照条件存在较大变化的情况下,依据已有方法得出的同一人脸的不同图像间的差异性可超过不同人脸的图像间的差异性,此时必然会得出错误的人脸识别结果。如上因素也是目前人脸识别技术未能如指纹识别技术一样得到普及的原因。
稀疏描述方法被引入到人脸识别领域后带来了人脸识别的突破性进展。已有的一些实验说明,在很多情况下包括图像存在噪声或被部分遮挡的情况下,稀疏描述方法可取得大大优于其他方法的分类正确率。实际上,基于稀疏描述方法的人脸识别技术是一个全新的方法学,它为人们解决已有的技术难点提供了一个新颖的视角和思路。但是,目前稀疏描述方法的图像识别应用仍有诸多问题需要解决。例如,现有的稀疏描述方法只利用了部分训练图像来表达测试图像,但是人们不知道究竟哪些训练图像被利用而哪些没被利用。又如,由于人脸图像的维数非常高,现有的稀疏描述方法的效率将非常低,在大容量训练集上尤其如此;该缺点使得方法不能应用于实际。本文针对如上问题,设计了几个新颖的基于稀疏描述思想的人脸识别方法,这些方法不仅计算效率高,而且能得到很高的识别精度。为解决现有的可见光人脸识别系统易受光照变化影响的问题,本文还设计并实现了一个基于人脸近红外与可见光图像的人脸识别系统。系统具有优异的性能。
In the past two decades face recognition has attracted much attention. People hope that the computer can have a powerful capability of using the face to recognize the identity as we can do and the world can become more‘intelligent’. However, up to now, automatic face recognition does not reach this goal of people. Especially under the complex condition, the performance of conventional face recognition technique that is constructed on the basis of the computation of the similarity between the face images cannot meet the requirement of real-world applications. For example, when the face is partially occluded, conventional face recognition technique usually incorrectly recognizes or rejects to recognize the face. The research also shows that under the condition of greatly varying lighting condition, the difference of the images of the same face obtained using conventional face recognition methods might be greater than the difference of two images respectively generated from two different faces. Consequently, this will lead to incorrect recognition result. Indeed this factor is also one of the reasons why the automatic face recognition technique is not as popular as the fingerprint recognition technique.
After the sparse representation method is applied to face recognition, a breakthrough has been made. Previous experiments show that under many cases including the case where the face is partially occluded or there is noise, the sparse method can obtain much higher classification accuracy than other methods. Indeed, sparse-representation-based face recognition technique is a novel methodology and it provides us a novel idea and viewpoint that is very useful for addressing the technical problems in face recognition. However, when the sparse representation method is applied to image recognition, there are still some unsolved problems. For example, previous sparse representation methods exploit only a portion of the whole training image set to represent the test image, but it is not known which training images are exploited and which are not. Moreover, because the dimension of the face image is very great, the computational efficiency of the sparse representation methods will be very low especially in the case where the training sample set has a large size. This is disadvantageous for the real-world applications. In order to solve the above issues, we devise several novel sparse-representation-idea-based face recognition methods. These methods not only are computationally efficient but also can obtain high accuracies. To address the issue that the performance of previous visible-light-based face recognition systems is affected by the varying illumination, we devise a face recognition system that combines visible light images and infrared red images of the face to robustly classify the face. This system has an excellent performance.
稀疏描述方法被引入到人脸识别领域后带来了人脸识别的突破性进展。已有的一些实验说明,在很多情况下包括图像存在噪声或被部分遮挡的情况下,稀疏描述方法可取得大大优于其他方法的分类正确率。实际上,基于稀疏描述方法的人脸识别技术是一个全新的方法学,它为人们解决已有的技术难点提供了一个新颖的视角和思路。但是,目前稀疏描述方法的图像识别应用仍有诸多问题需要解决。例如,现有的稀疏描述方法只利用了部分训练图像来表达测试图像,但是人们不知道究竟哪些训练图像被利用而哪些没被利用。又如,由于人脸图像的维数非常高,现有的稀疏描述方法的效率将非常低,在大容量训练集上尤其如此;该缺点使得方法不能应用于实际。本文针对如上问题,设计了几个新颖的基于稀疏描述思想的人脸识别方法,这些方法不仅计算效率高,而且能得到很高的识别精度。为解决现有的可见光人脸识别系统易受光照变化影响的问题,本文还设计并实现了一个基于人脸近红外与可见光图像的人脸识别系统。系统具有优异的性能。
In the past two decades face recognition has attracted much attention. People hope that the computer can have a powerful capability of using the face to recognize the identity as we can do and the world can become more‘intelligent’. However, up to now, automatic face recognition does not reach this goal of people. Especially under the complex condition, the performance of conventional face recognition technique that is constructed on the basis of the computation of the similarity between the face images cannot meet the requirement of real-world applications. For example, when the face is partially occluded, conventional face recognition technique usually incorrectly recognizes or rejects to recognize the face. The research also shows that under the condition of greatly varying lighting condition, the difference of the images of the same face obtained using conventional face recognition methods might be greater than the difference of two images respectively generated from two different faces. Consequently, this will lead to incorrect recognition result. Indeed this factor is also one of the reasons why the automatic face recognition technique is not as popular as the fingerprint recognition technique.
After the sparse representation method is applied to face recognition, a breakthrough has been made. Previous experiments show that under many cases including the case where the face is partially occluded or there is noise, the sparse method can obtain much higher classification accuracy than other methods. Indeed, sparse-representation-based face recognition technique is a novel methodology and it provides us a novel idea and viewpoint that is very useful for addressing the technical problems in face recognition. However, when the sparse representation method is applied to image recognition, there are still some unsolved problems. For example, previous sparse representation methods exploit only a portion of the whole training image set to represent the test image, but it is not known which training images are exploited and which are not. Moreover, because the dimension of the face image is very great, the computational efficiency of the sparse representation methods will be very low especially in the case where the training sample set has a large size. This is disadvantageous for the real-world applications. In order to solve the above issues, we devise several novel sparse-representation-idea-based face recognition methods. These methods not only are computationally efficient but also can obtain high accuracies. To address the issue that the performance of previous visible-light-based face recognition systems is affected by the varying illumination, we devise a face recognition system that combines visible light images and infrared red images of the face to robustly classify the face. This system has an excellent performance.
引文
1徐勇.几种线性与非线性特征抽取方法及人脸识别应用.南京理工大学博士学位论文, 2004
2 Z. Jin, J. Yang, Z. Hu and Z. Lou. Face Recognition Based on the Uncorrelated Discriminant Transformation. Pattern Recognition 34(7), 2001: 1405-1416
3 J. Wright, Y. Ma, J. Mairal. Sparse Representation for Computer Vision and Pattern Recognition. Proceedings of IEEE, 2009: 1-8
4 J. Wright, A. Y. Yang, A. Ganesh. Robust Face Recognition Via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 2009: 210-227
5 K. L. Kroeker. Face Recognition Breakthrough. Communications of the ACM, 52(8), 2009: 19
6 V. Cevher, A. C. Sankaranarayanan, M. F. Duarte, D. Reddy, R. G. Baraniuk and R. Chellappa. Compressive Sensing for Background Subtraction. In ECCV, Marseille, France, 2008: 12–18
7 M. Dikmen, T. Huang. Robust Estimation of Foreground in Surveillance Videos by Sparse Error Estimation. In Proceedings of International Conference on Pattern Recognition, 2008: 1-4
8 E. Elhamifar, R. Vidal. Sparse Subspace Clustering. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2009: 2790-2797
9 S. Rao, R. Tron, R. Vidal, Y. Ma. Motion Segmentation Via Robust Subspace Separation in the Presence of Outlying, Incomplete, or Corrupted Trajectories. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2008: 1-8
10 J. Mairal, F. Bach, J. Ponce G. Sapiro and A. Zisserman. Supervised Dictionary Learning, NIPS, 2008: 1033-1040
11 Y. Shi, D. Q. Dai, C. C. Liu and H. Yan. Sparse Discriminant Analysis for Breast Cancer Biomarker Identification and Classification. Progress in Natural Science, 19(11), 2009: 1635-1641
12 E. Cand`es, T. Tao. Near-optimal Signal Recovery from Random Projections: Universal Encoding Strategies? IEEE Trans. Information Theory, 52(12), 2006:5406–5425
13 E. Cand`es, J. Romberg and T. Tao. Stable Signal Recovery from Incomplete and Inaccurate Measurements. Comm. on Pure and Applied Math, 59(8), 2006: 1207–1223
14 Y. Shi, D. Q. Dai, C. X. Ren and M. Y. Wu. Gene Selection and Visualization Based on Sparse Maximal Margin Features. Chinese Conference on Pattern Recognition, CCPR'09, Nanjing, Date: 5-6 Nov. 2009
15 D. Donoho. For Most Large Underdetermined Systems of Linear Equations the Minimal L1-norm Solution is Also The Sparsest Solution. Comm. On Pure and Applied Math, 59(6), 2006: 797–829
16 P. Zhao, B. Yu. On Model Selection Consistency of Lasso. Journal of Machine Learning Research, 7, 2006: 2541–2567
17 S. S. Chen,D. L. Donoho and M. A. Saunders. Atomic Decomposition by Basis Pursuit. SIAM Journal on Scientific Computing, 20(1), 1998: 33– 61
18 K. Kreutz-Delgado, JF. Murray, BD. Rao and K. Engan. Dictionary Learning Algorithms for Sparse Representation. Neural Computation 15, 2003: 349–396
19 D. L. Donoho, M. Elad. Optimally Sparse Representation Via Minimization. Processing, National. Academy Science, 100, 2003: 2197–2202
20 Y. Li, A. Cichocki and S. Amari. Analysis of Sparse Representation and Blind Source Separation. Neural Computation, 16(6), 2004: 1193-1234
21 M. Elad, M. Aharon. Image Denoising Via Sparse and Redundant Representations over Learned Dictionaries. IEEE Transactions on Image Processing, 15 (12), 2006 : 3736-3745
22 F.J. Theis, G. A. García. On the Use of Sparse Signal Decomposition in the Analysis of Multi-channel Surface Electromyograms. Signal Processing 86(3), 2006: 603-623
23 M. Ranzato, C. Poultney, S. Chopra and Y. LeCun. Efficient Learning of Sparse Representations with an Energy-based model. In Neural Information Processing Systems, 2006
24 M. Aharon, M. Elad and A. Bruckstein. The k-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation. IEEE Trans. on Signal Processing, 54(11), 2006: 4311–4322
25 M. Zhou, H. Chen, J. Paisley, L. Ren, G. Sapiro and L. Carin. Non-parametric Bayesian Dictionary Learning for Sparse Image Representations. In NeuralInformation Processing Systems (NIPS), 2009
26 J. Mairal, M. Elad and G. Sapiro. Sparse Representation for Color Image Restoration. IEEE Trans. Image Processing, 17, 2008
27 A. Y. Yang, J. Wright and Y. Ma. Feature Selection in Face Recognition: A sparse representation perspective. IEEE Transactions, 2007
28 J. Mutch, D. Lowe. Multiclass Object Recognition with Sparse, Localized Features. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2006: 17–22
29 S. Agarwal, A. Awan and D. Roth. Learning to Detect Objects in Images Via a Sparse, Part-based Representation. PAMI, 26(11), 2004:1475–1490
30 H. Jia, A. M. Martinez. Face Recognition with Occlusions in The Training And Testing Sets. Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, 2008
31邱天爽,毕晓辉.稀疏分量分析在欠定盲源分离问题中的研究进展及应用.通信学报,24(6),2008: 966 ~ 970
32 P.J, RAUSS.P, DER.S. FERET. (Face Recognition Technology) Recognition Algorithm Development and Test Results. ARL2TR2995 [R]. [S. l.] : Army Research Laboratory, 1996
33吴巾一,周德龙.人脸识别方法综述.计算机应用研究, 26(9):3205-3209
34 http://hi.baidu.com/netee/blog/item/ef7b94234b228548925807c9.html
35 PH IPL IPS P J, GROTHER P, M ICHEAL R J. FRVT 2002 evaluation report[ R /OL ]. ( 2003203 ) . http: / /www. frvt.org/DLs/FRVT_2002_Evaluation _Report. pdf
36王跃明,潘纲,吴朝晖.三维人脸识别研究综述.计算机辅助设计与图形学学报, 20(7): 819-829,2008
37 http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
38 http://cvc.yale.edu/projects/yalefaces/yalefaces.html
39 http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html
40 Y. Yoshitomi, S. Kimura, E. Hira and S. Tomita. Facial Expression Recognition Using Infrared Rays Image Processing. Processing of the Annual Conventzon IPS Japan, No. 2, 1996: 339-340
41 Y. Yoshitomi, T. Miyaura, S. Tomita and S. Kimura. Face Identification Using Thermal Image Processing. Processing IEEE Int. Workshop Robot Hum. Commun, 1997: 374–379
42 F. J. Prokoski. History, Current Status, and Future of Infrared Identification. Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, 2000: 5-14
43 Selinger, D. Socolinsky. Appearance-based Facial Recognition Using Visible and Thermal Timagery: A Comparative Study. Equinox Corporation no. 02–01, Technical Report, 2002
44 Z. Pan, G. Healey, M. Prasad and B. Tromberg. Face Recognition in Hyperspectral Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (12), 2000: 1552–1560
45 P. Buddharaju, I.T. Pavlidis and P. Tsiamyrtzis. Pose-Invariant Physiological Face Recognition in the Thermal Infrared Spectrum. IEEE Conference on Computer Vision and Pattern Recognition Workshop, New York, 2006: 53-53
46 M. Akhloufi, A. Bendada,Thermal Faceprint. A New Thermal Face Signature Extraction For Infrared Face Recognition. CRV 2008: Fifth Canadian Conference on Computer and Robot Vision, Windsor, Ontario, 28-30 May, 2008: 269-272
47 D. Pereira, M. P. Fargues and G. Karunasiri. Investigation of Uncooled Infrared Imagery for Face Recognition. SPIE Optical Engineering 45(1), 2006: 016401-1-016401-6
48 D. A. Socolinskyy, A. Selingerz. Comparative Study of Face Recognition Performance with Visible and Thermal Infrared Imagery. International Conference on Pattern Recognition, 2002: 217–222
49 D. Socolinsky, L. Wolff, J. Neuheisel and C. Eveland. Illumination Invariant Face Recognition Using Thermal Infrared Imagery. Computer Vision and Pattern Recognition, Vol. 1, 2001: 527-534
50 D. Socolinsky, A. Selinger, J. Neuheisel. Face Recognition with Visible and Thermal Infrared Imagery. Computer Vision and Image Understanding, Vol. 91, No.1-2, 2003: 72-114
51 J. Wilder, J. Phillips, C. Jiang and S. Wiener. Comparison of Visible and Infrared Imagery for Face Recognition. Second International Conference on Automatic Face and Gesture Recognition, Killington, 1996: 182–187
52 G. Bebis, A. Gyaourova, S. Singh and I. Pavlidis. Face Recognition by Fusing Thermal Infrared and Visible Imagery. Image and Vision Computing, Vol. 24, Issue 7, 2006: 727-742
53 Y. Xu, D. Zhang, J. Yang and J. Yang. An Approach for Directly Extracting Featuresfrom Matrix Data and Its Application in Face Recognition, Neurocomputing, 2008: 71, 10-12, 1857-1865.
54 D. Zhang, F. Song, Y. Xu and Z. Liang. Advanced Pattern Recognition Technologies with Applications to Biometrics. Medical Information Science Reference, 2009
55 Y. Xu, Z. Jin. Down-sampling Face Images and Low-resolution Face Recognition. The third international conference on innovative computing, information and control, Dalian, China, 2008: 392-395
2 Z. Jin, J. Yang, Z. Hu and Z. Lou. Face Recognition Based on the Uncorrelated Discriminant Transformation. Pattern Recognition 34(7), 2001: 1405-1416
3 J. Wright, Y. Ma, J. Mairal. Sparse Representation for Computer Vision and Pattern Recognition. Proceedings of IEEE, 2009: 1-8
4 J. Wright, A. Y. Yang, A. Ganesh. Robust Face Recognition Via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2), 2009: 210-227
5 K. L. Kroeker. Face Recognition Breakthrough. Communications of the ACM, 52(8), 2009: 19
6 V. Cevher, A. C. Sankaranarayanan, M. F. Duarte, D. Reddy, R. G. Baraniuk and R. Chellappa. Compressive Sensing for Background Subtraction. In ECCV, Marseille, France, 2008: 12–18
7 M. Dikmen, T. Huang. Robust Estimation of Foreground in Surveillance Videos by Sparse Error Estimation. In Proceedings of International Conference on Pattern Recognition, 2008: 1-4
8 E. Elhamifar, R. Vidal. Sparse Subspace Clustering. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2009: 2790-2797
9 S. Rao, R. Tron, R. Vidal, Y. Ma. Motion Segmentation Via Robust Subspace Separation in the Presence of Outlying, Incomplete, or Corrupted Trajectories. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2008: 1-8
10 J. Mairal, F. Bach, J. Ponce G. Sapiro and A. Zisserman. Supervised Dictionary Learning, NIPS, 2008: 1033-1040
11 Y. Shi, D. Q. Dai, C. C. Liu and H. Yan. Sparse Discriminant Analysis for Breast Cancer Biomarker Identification and Classification. Progress in Natural Science, 19(11), 2009: 1635-1641
12 E. Cand`es, T. Tao. Near-optimal Signal Recovery from Random Projections: Universal Encoding Strategies? IEEE Trans. Information Theory, 52(12), 2006:5406–5425
13 E. Cand`es, J. Romberg and T. Tao. Stable Signal Recovery from Incomplete and Inaccurate Measurements. Comm. on Pure and Applied Math, 59(8), 2006: 1207–1223
14 Y. Shi, D. Q. Dai, C. X. Ren and M. Y. Wu. Gene Selection and Visualization Based on Sparse Maximal Margin Features. Chinese Conference on Pattern Recognition, CCPR'09, Nanjing, Date: 5-6 Nov. 2009
15 D. Donoho. For Most Large Underdetermined Systems of Linear Equations the Minimal L1-norm Solution is Also The Sparsest Solution. Comm. On Pure and Applied Math, 59(6), 2006: 797–829
16 P. Zhao, B. Yu. On Model Selection Consistency of Lasso. Journal of Machine Learning Research, 7, 2006: 2541–2567
17 S. S. Chen,D. L. Donoho and M. A. Saunders. Atomic Decomposition by Basis Pursuit. SIAM Journal on Scientific Computing, 20(1), 1998: 33– 61
18 K. Kreutz-Delgado, JF. Murray, BD. Rao and K. Engan. Dictionary Learning Algorithms for Sparse Representation. Neural Computation 15, 2003: 349–396
19 D. L. Donoho, M. Elad. Optimally Sparse Representation Via Minimization. Processing, National. Academy Science, 100, 2003: 2197–2202
20 Y. Li, A. Cichocki and S. Amari. Analysis of Sparse Representation and Blind Source Separation. Neural Computation, 16(6), 2004: 1193-1234
21 M. Elad, M. Aharon. Image Denoising Via Sparse and Redundant Representations over Learned Dictionaries. IEEE Transactions on Image Processing, 15 (12), 2006 : 3736-3745
22 F.J. Theis, G. A. García. On the Use of Sparse Signal Decomposition in the Analysis of Multi-channel Surface Electromyograms. Signal Processing 86(3), 2006: 603-623
23 M. Ranzato, C. Poultney, S. Chopra and Y. LeCun. Efficient Learning of Sparse Representations with an Energy-based model. In Neural Information Processing Systems, 2006
24 M. Aharon, M. Elad and A. Bruckstein. The k-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation. IEEE Trans. on Signal Processing, 54(11), 2006: 4311–4322
25 M. Zhou, H. Chen, J. Paisley, L. Ren, G. Sapiro and L. Carin. Non-parametric Bayesian Dictionary Learning for Sparse Image Representations. In NeuralInformation Processing Systems (NIPS), 2009
26 J. Mairal, M. Elad and G. Sapiro. Sparse Representation for Color Image Restoration. IEEE Trans. Image Processing, 17, 2008
27 A. Y. Yang, J. Wright and Y. Ma. Feature Selection in Face Recognition: A sparse representation perspective. IEEE Transactions, 2007
28 J. Mutch, D. Lowe. Multiclass Object Recognition with Sparse, Localized Features. Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, 2006: 17–22
29 S. Agarwal, A. Awan and D. Roth. Learning to Detect Objects in Images Via a Sparse, Part-based Representation. PAMI, 26(11), 2004:1475–1490
30 H. Jia, A. M. Martinez. Face Recognition with Occlusions in The Training And Testing Sets. Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, 2008
31邱天爽,毕晓辉.稀疏分量分析在欠定盲源分离问题中的研究进展及应用.通信学报,24(6),2008: 966 ~ 970
32 P.J, RAUSS.P, DER.S. FERET. (Face Recognition Technology) Recognition Algorithm Development and Test Results. ARL2TR2995 [R]. [S. l.] : Army Research Laboratory, 1996
33吴巾一,周德龙.人脸识别方法综述.计算机应用研究, 26(9):3205-3209
34 http://hi.baidu.com/netee/blog/item/ef7b94234b228548925807c9.html
35 PH IPL IPS P J, GROTHER P, M ICHEAL R J. FRVT 2002 evaluation report[ R /OL ]. ( 2003203 ) . http: / /www. frvt.org/DLs/FRVT_2002_Evaluation _Report. pdf
36王跃明,潘纲,吴朝晖.三维人脸识别研究综述.计算机辅助设计与图形学学报, 20(7): 819-829,2008
37 http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
38 http://cvc.yale.edu/projects/yalefaces/yalefaces.html
39 http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html
40 Y. Yoshitomi, S. Kimura, E. Hira and S. Tomita. Facial Expression Recognition Using Infrared Rays Image Processing. Processing of the Annual Conventzon IPS Japan, No. 2, 1996: 339-340
41 Y. Yoshitomi, T. Miyaura, S. Tomita and S. Kimura. Face Identification Using Thermal Image Processing. Processing IEEE Int. Workshop Robot Hum. Commun, 1997: 374–379
42 F. J. Prokoski. History, Current Status, and Future of Infrared Identification. Proceedings IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications, 2000: 5-14
43 Selinger, D. Socolinsky. Appearance-based Facial Recognition Using Visible and Thermal Timagery: A Comparative Study. Equinox Corporation no. 02–01, Technical Report, 2002
44 Z. Pan, G. Healey, M. Prasad and B. Tromberg. Face Recognition in Hyperspectral Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (12), 2000: 1552–1560
45 P. Buddharaju, I.T. Pavlidis and P. Tsiamyrtzis. Pose-Invariant Physiological Face Recognition in the Thermal Infrared Spectrum. IEEE Conference on Computer Vision and Pattern Recognition Workshop, New York, 2006: 53-53
46 M. Akhloufi, A. Bendada,Thermal Faceprint. A New Thermal Face Signature Extraction For Infrared Face Recognition. CRV 2008: Fifth Canadian Conference on Computer and Robot Vision, Windsor, Ontario, 28-30 May, 2008: 269-272
47 D. Pereira, M. P. Fargues and G. Karunasiri. Investigation of Uncooled Infrared Imagery for Face Recognition. SPIE Optical Engineering 45(1), 2006: 016401-1-016401-6
48 D. A. Socolinskyy, A. Selingerz. Comparative Study of Face Recognition Performance with Visible and Thermal Infrared Imagery. International Conference on Pattern Recognition, 2002: 217–222
49 D. Socolinsky, L. Wolff, J. Neuheisel and C. Eveland. Illumination Invariant Face Recognition Using Thermal Infrared Imagery. Computer Vision and Pattern Recognition, Vol. 1, 2001: 527-534
50 D. Socolinsky, A. Selinger, J. Neuheisel. Face Recognition with Visible and Thermal Infrared Imagery. Computer Vision and Image Understanding, Vol. 91, No.1-2, 2003: 72-114
51 J. Wilder, J. Phillips, C. Jiang and S. Wiener. Comparison of Visible and Infrared Imagery for Face Recognition. Second International Conference on Automatic Face and Gesture Recognition, Killington, 1996: 182–187
52 G. Bebis, A. Gyaourova, S. Singh and I. Pavlidis. Face Recognition by Fusing Thermal Infrared and Visible Imagery. Image and Vision Computing, Vol. 24, Issue 7, 2006: 727-742
53 Y. Xu, D. Zhang, J. Yang and J. Yang. An Approach for Directly Extracting Featuresfrom Matrix Data and Its Application in Face Recognition, Neurocomputing, 2008: 71, 10-12, 1857-1865.
54 D. Zhang, F. Song, Y. Xu and Z. Liang. Advanced Pattern Recognition Technologies with Applications to Biometrics. Medical Information Science Reference, 2009
55 Y. Xu, Z. Jin. Down-sampling Face Images and Low-resolution Face Recognition. The third international conference on innovative computing, information and control, Dalian, China, 2008: 392-395