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Publications

Recent publications by BIIC researchers:

  1. Amol S. Joshi, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi, "FDeblur-GAN Fingerprint Deblurring Using Generative Adversarial Network," IEEE Int. Joint Conference on Biometrics (IJCB'21), Aug. 4-7, 2021. (Acceptance rate 40.2%)
  2. Poorya Aghdaie, Baaria Chaudhary, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi, "Attention Aware Wavelet-based Detection of Morphed Face Images," IEEE Int. Joint Conference on Biometrics (IJCB'21), Aug. 4-7, 2021. (Acceptance rate 40.2%)
  3. Baaria Chaudhary, Poorya Aghdaie, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi, “Differential Morph Face Detection using Discriminative Wavelet Sub-bands,” IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop on Biometrics (CVPRW),” Virtual, June 19-25, 2021.
  4. Fariborz Taherkhani, Ali Dabouei, Sobhan Soleymani, J. Dawson, N. M. Nasrabadi, “Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),” Virtual, June 19-25, 2021. (Acceptance rate 27%)
  5. Ali Dabouei, Sobhan Soleymani, Fariborz Taherkhani, Nasser M. Nasrabadi, “SuperMix: Supervising the Mixing Data Augmentation,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),” Virtual, June 19-25, 2021. (Acceptance rate 27%)
  6. Poorya Aghdaie, Baaria Chaudhary, Sobhan Soleymani, Jeremy Dawson, Nasser M. Nasrabadi, “Detection of Morphed Face Images Using Discriminative Wavelet Sub-bands,” the 9th IEEE International Workshop on Biometrics and Forensics (IWBF21), May 6-7, 2021, Rome, Italy.
  7.  Sobhan Soleymani, Baaria Chaudhary, Ali Dabouei, Jeremy Dawson, Nasser M. Nasrabadi, “Differential Morphed Face Detection Using Deep Siamese Networks,” ICPR Workshop, MultiMedia FORensics in the WILD (MMForWILD’2020), Jan. 10-15, 2021, Milan, Italy.
  8. Sobhan Soleymani, Ali Dabouei, Fariborz Taherkhani, Jeremy Dawson and Nasser M. Nasrabadi, “Mutual Information Maximization on Disentangled Representations for Differential Morph Detection,” IEEE Winter Conference on Applications of Computer Vision (WACV 2021), March 5-9, 2021, Waikoloa, Hawaii.
  9. Domenick Poster, Matthew Thielke, Robert Nguyen, Srinivasan Rajaraman, Xing Di, Cedric Nimpa Fondje, Vishal Patel, Nathan J. Short, Benjamin S. Riggan, Nasser M. Nasrabadi, Shuowen Hu “A Large-Scale, Time-Sychronized Visible and Thermal Face Dataset,” IEEE Winter Conference on Applications of Computer Vision (WACV 2021), March 5-9, 2021, Waikoloa, Hawaii.
  10. Domenick D. Poster, Shuowen Hu, Nathan J. Short, Benjamin S. Riggan, and Nasser M. Nasrabadi, “Visible-to-Thermal Transfer Learning for Facial Landmark Detection,” IEEE Access, vol. 8, issue 1, pp. 82306-82319, Dec. 2021.
  11. Sertac Arisoy, Nasser M. Nasrabadi, and Koray Kayabol “Unsupervised Pixel-wise Hyperspectral Anomaly Detection via Autoencoding Adversarial Networks,” IEEE Geoscience and Remote Sensing Letters, vol. 0, no. 0, pp , 2021.
  12. Seyed Mehdi Iranmanesh, Nasser M. Nasrabadi, “HGAN: Hybrid Generative Adversarial Network,” Journal of Intelligent & Fuzzy Systems, Vol. 41-1, Feb. 2021.
  13. Veeru Talreja, Matthew Valenti, and Nasser Nasrabadi, “Deep Hashing for Secure Multimodal Biometrics,” IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1306-1321, Oct. 22, 2021. 10.1109/TIFS.2020.3033189
  14. Moktari Mostofa, Syeda Nyma Ferdous, Benjamin S. Riggan, and Nasser M. Nasrabadi, “Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network,” IEEE Access, vol. 8, issue 1, pp. 82306-82319, Dec. 2020.
  15. Fariborz Taherkhani, Veeru Talreja, Matthew Valenti, and Nasser M. Nasrabadi, “Error Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval,” IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 2, no. 3, July 2020.
  16. Seyed Mehdi Iranmanesh, Benjamin Riggan, Shuowen Hu, Nasser M. Nasrabadi, “Coupled Generative Adversarial Network for Heterogeneous Face Recognition,” Image and Vision Computing, vol. 94, February 2020.
  17. Ali Takbiri-Borujeni, Hadi Kazemi, Nasser Nasrabadi, “A data-driven surrogate to image-based flow simulations in porous media,” Computers & Fluids, Volume 201, 15 April 2020, 104475, https://doi.org/10.1016/j.compfluid.2020.104475.
  18. Xiaoxia Sun, Nasser M. Nasrabadi and Trac D. Tran, "Supervised Deep Sparse Coding Networks for Image Classification,"accepted will appear in IEEE Trans. on Image Processing, 2019.
  19. Nasser M. Nasrabadi, “Deep Target: An Automatic Target Recognition using Deep Convolutional Neural Networks,” IEEE Transactions on Aerospace and Electronic Systems, 18 Jan. 2019. DOI: 10.1109/TAES.2019.2894050 
  20. Zhangming Ding, Nasser M. Nasrabadi, Yun Fu, “Semi-supervised Task-driven Deep Transfer Learning via Coupled Neural Networks,” IEEE Transaction on Image Processing, vol. 27, issue 11, pp. 5214-5224, June 2018.
  21. Amirsina Torfi, SM Iranmanesh, Nasser M. Nasrabadi, Jeremy Dawson, “3D Convolutional Neural Networks for Cross Audio-Visual Matching Recognition,” IEEE Access, vol. 5, pp. 22081-22091, 2017.
  22. Tianpei Xie, Nasser M. Nasrabadi, Alfred O. Hero III, “Learning to Classify with Possible Sensor Failures,” IEEE Transactions on Signal Processing, vol. 65, no.4, pp. 836-849, 2017.
  23. Minh Dao, Nam Nguyen, Nasser M. Nasrabadi, and Trac D. Tran, “Multi-sensor classification via joint sparse representation for discriminating between human and animal footsteps,” IEEE Trans. on Signal Processing, Vol. 64, No. 9, pp. 2400-2415, May 2016.
  24. Soheil Bahrampour, Nasser M. Nasrabadi, Asok Ray, and Kenneth W. Jenkins, “Multimodal task-driven dictionary learning,” IEEE Trans. on Image Processing, vol. 25, no. 1, pp. 24-38 Jan. 2016.
  25.  Benjamin S. Riggan, Christopher Reale, and Nasser M. Nasrabadi, “Coupled auto-associative neural networks for heterogeneous face recognition,” IEEE Access, vol. 3, pp. 1620-1632, Oct. 2015.
  26. Sumit Shekhar, Vishal M. Patel, Nasser M. Nasrabadi and Rama Chellappa, “Joint sparse representation for robust multimodal biometrics recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 36, no. 1, pp. 113-126, Jan. 2014.
  27. D. Poster, B. S. Riggan, S. Hu, N. M. Nasrabadi, “An Examination of Deep-Learning Based Landmark Detection Methods on Thermal Face Imagery,” CVPRW’19, 15th IEEE Workshop on Perception Beyond the Visible Spectrum (PBVS’19), 2019.
  28. F. Taherkhani, J. Dawson, N. M. Nasrabadi, “Hyperspectral Band Selection for Face Recognition Based on a Structurally Sparsified Deep Convolutional Neural Networks,” in the 12th IAPR International Conference On Biometrics (ICB’19), 4-7 June 2019, Crete, Greece.
  29. S. Soleymani, A. Dabouei, J. Dawson, N. M. Nasrabadi, “Adversarial Examples to Fool Iris Recognition Systems” in the 12th IAPR International Conference On Biometrics (ICB’19), 4-7 June 2019, Crete, Greece.
  30.  A. Dabouei, S. Soleymani, J. Dawson, N. M. Nasrabadi, “Deep Contactless Fingerprint Unwarping,” in the 12th IAPR International Conference On Biometrics (ICB’19), 4-7 June 2019, Crete, Greece.
  31. S. M. Iranmanesh, N. M. Nasrabadi, “Attribute-Guided Deep Polarimetric Thermal-to-visible Face Recognition,” in the 12th IAPR International Conference On Biometrics (ICB’19), 4-7 June 2019, Crete, Greece.
  32. V. Talreja, M. Valenti, N. M. Nasrabadi, “Learning to Authenticate with Deep Multibiometric Hashing and Neural Network Decoding," IEEE International Conference on Communications (ICC’19), 20-24 May 2019, Shanghai, China.
  33. F. Taherkhani, H. Kazemi, N. M. Nasrabadi  “Matrix Completion for Graph-Based Deep Semi-Supervised Learning”, 33rd AAAI Conference on Artificial Intelligence (AAAI), Jan. 27-Feb. 1, 2019, Honolulu, Hawaii.
  34. A. Dabouei, S, Soleymani, J. Dawson, N. M.  Nasrabadi, “Fast Geometrically-Perturbed Adversarial Faces,” 2019 IEEE Winter Conference on Applications of Computer Vision ( WACV’19), Waikoloa, Hawaii,  January 8-10, 2019.
  35. H. Kazemi, S. M. Iranmanesh, N. M. Nasrabadi, “Style and Content Disentanglement in Generative Adversarial Networks,” 2019 IEEE Winter Conference on Applications of Computer Vision (WACV’19), Waikoloa, Hawaii,  January 8-10, 2019.
  36. Zhiguo Cao Chi Li, Yang Xiao and Xin Li . “Despeckling via deep residue learning: a hybrid approach,” submitted to IEEE Signal Processing Letter, 2018.
  37. Weisheng Dong, Huan Wang, Fangfang Wu, Guangming Shi and X. Li , “Deep Spatial-spectral representation learning for hyperspectral image denoising,” submitted to IEEE Trans. on Computational Imaging, 2018.
  38. X. Zhou, K. Jin, M. Xu, G. Guo, “Learning deep compact similarity metric for kinship verification from face images,” Information Fusion, 48, 84-94, 2018.
  39. Z. Tan, J. Wan, Z. Lei, R. Zhi, G. Guo, S. Z. Li, “Efficient group-n encoding and decoding for facial age estimation,” IEEE transactions on pattern analysis and machine intelligence, 40 (11), 2610, 2018.
  40. M. Al Jazaery, G. Guo, “Video-based depression level analysis by encoding deep spatiotemporal features,” IEEE Transactions on Affective Computing, 2018.
  41. M. Barr, G. Guo, S. Colby, M. Olfert, “Detecting body mass index from a facial photograph in lifestyle intervention,” Technologies 6 (3), 83, 2018.
  42. X. Zhou, K. Jin, Y. Shang, G. Guo, “Visually interpretable representation learning for depressionrecognition from facial images,” IEEE Transactions on Affective Computing, 2018.
  43. J. Wan, Z. Tan, Z. Lei, G. Guo, S. Z. Li, “Auxiliary demographic information assisted age estimation with cascaded structure,” IEEE Transactions on Cybernetics, 1-11, 2018.
  44. D. P. Chowdhury, S. Bakshi, G. Guo, P. K. Sa, “On applicability of tunable filter bank based feature for ear biometrics: a study from constrained to unconstrained,” Journal of Medical Systems 42 (1), 11, 2018.
  45. Junxiang Wang, Liang Zhao, Yanfang Ye, Yuji Zhang. “Adverse event detection by integrating twitter data and VAERS”, Journal of Biomedical Semantics , 9:19, 2018.
  46. Yanfang Ye, Lingwei Chen, Shifu Hou, William Hardy, Xin Li. “DeepAM: a heterogeneous deep learning framework for intelligent malware detection.” Knowledge and Information Systems, 54.2 (2018): 265-285. 
  47. Hara K., Adams A., Milland K., Savage S., Callison-Burch C., Bigham J., “A Data-Driven Analysis of Workers Earnings on Amazon Mechanical Turk.” CHI 2018: ACM Conference on Human Factors in Computing Systems, 25 percent acceptance rate and Honorable Mention (only 5 percent of all accepted papers).
  48. Saviaga C., Keegan B., Savage S., “Mobilizing the Trump Train: Understanding Collective Action in a Political Troll Community.” ICWSM 2018: International AAAI Conference on Web and Social Media 2018, 15 percent acceptance rate.
  49. Chun-Wei Chiang, Anna Kasunic, Saiph Savage, “Crowd Coach: Peer Coaching for Crowd Workers' Skill Growth,” CSCW: ACM Conference on Computer-Supported Cooperative Work, 2018.
  50. Juan Pablo Flores, Saiph Savage, Jessica Hammer, Joseph Seering, “The Social Roles of Bots: Evaluating Impact of Bots on Discussions in Online Communities,” CSCW: ACM Conference on Computer-Supported Cooperative Work, 2018.
  51. Yixin Du and Xin Li, “Recursive deep residue learning for single image dehazing,” IEEE Conference on CVPR Workshop, 2018. 
  52. G Guo, N Zhang, “What Is the Challenge for Deep Learning in Unconstrained Face Recognition?,”13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018.
  53. X. Liu, G Guo, “Attributes in Multiple Facial Images,” 13th IEEE International Conference on Automatic Face & Gesture Recognition , 2018.
  54. Yuyang Gao, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, Chaowei Yang. “Incomplete Label Multi-task Deep Learning for Spatio-temporal Event Subtype Forecasting”, 33rd AAAI Conference on Artificial Intelligence, 2019 (16.2 percent acceptance rate).
  55. Yanfang Ye, Lingwei Chen, Shifu Hou, Xin Li, Shouhuai Xu, Liang Zhao, Jiabin Wang, Qi Xiong. “ICSD: An Automatic System for Insecure Code Snippet Detection in Stack Overflow over Heterogeneous Information Network”, Annual Computer Security Applications Conference, 2018 (20.1 percent acceptance rate).
  56. Yujie Fan, Shifu Hou, Yiming Zhang, Yanfang Ye, Melih Abdulhayoglu. “Gotcha - Sly Malware! Scorpion: A Metagraph2vec Based Malware Detection System”, Proceedings of ACM International Conference on Knowledge Discovery and Data Mining , 2018 (22.5 percent acceptance rate).
  57. Yujie Fan, Yiming Zhang, Yanfang YeXin Li. “Automatic Opioid User Detection from Twitter: Transductive Ensemble Built on Different Meta-graph Based Similarities over Heterogeneous Information Network,”  27th International Joint Conference on Artificial Intelligence, 2018 (20.5 percent acceptance rate).
  58. Shifu Hou, Yanfang Ye, Yangqiu Song, Melih Abdulhayoglu. “Make Evasion Harder: An Intelligent Android Malware Detection System,” 27th International Joint Conference on Artificial Intelligence, 2018 (20.5 percent acceptance rate).
  59. Junxiang Wang, Liang Zhao, Yanfang Ye. “Semi-supervised Multi-instance Learning for Flu Shot Adverse Event Detection,”  IEEE international conference on Big Data , 2018 (18.9 percent acceptance rate).
  60. Yiming Zhang, Yujie Fan, Shifu Hou, Jian Liu*, Yanfang Ye, Thirimachos Bourlai. “iDetector: Automate Underground Forum Analysis Based on Heterogeneous Information Network,” Proceedings of International Conference on Advances in Social Network Analysis and Mining, 2018.
  61. Lingwei Chen, Shifu Hou, Yanfang Ye, Shouhuai Xu. “DroidEye: Fortifying Security of Learning-based Classifier against Adversarial Android Malware Attacks,”  Proceedings of International Conference on Advances in Social Network Analysis and Mining, 2018.
  62. Yiming Zhang, Yujie Fan, Yanfang Ye, Liang Zhao, Jiabin Wang, Qi Xiong, Fudong Shao. “KADetector: Automatic Identification of Key Actors in Online Hack Forums Based on Structured Heterogeneous Information Network,” IEEE International Conference on Big Knowledge, 2018.
  63. Yiming Zhang, Yujie Fan*, Yanfang YeXin LiErin L. Winstanley. “Utilizing Social Media to Combat Opioid Addiction Epidemic: Automatic Detection of Opioid Users from Twitter,” 32nd AAAI Conference on Artificial Intelligence Workshops , 2018.
  64. A.S. Bommagani, M.C. Valenti, and A. Ross, “A framework for secure cloud-empowered mobile biometrics,” in Proc. IEEE Military Commun. Conf. (MILCOM), Oct. 2014.
  65. M.C. Valenti, S. Talarico, and P. Rost, “The role of computational outage in dense cloud-based centralized radio access networks,” in Proc. IEEE Global Commun. Conf.(GLOBECOM), Dec. 2014.
  66. M. Fanaei, A. Tahmasbi-Sarvestani, Y.P. Fallah, G. Bansal, M.C. Valenti, and J. Kenny, “Adaptive content control for communication amongst cooperative automated vehicles,” in Proc. IEEE International Symposium on Wireless Vehicular Communications (WIVEC), Sept. 2014.
  67. M. Fanaei, M.C. Valenti, and N. Schmid, “Effects of spatial randomness on locating a point source with distributed sensors,” in Proc. IEEE Int. Conf. on Commun. (ICC) Workshop on Advances in Network Localization and Navigation (ANLN), June 2014, invited paper.
  68. S. Talarico, N.A. Schmid, M. Alkhweldi, and M.C. Valenti, “Distributed estimation of a parametric field: Algorithms and performance analysis,” IEEE Transactions on Signal Processing, vol. 62, no. 5, pp. 1041-1053, Mar 1, 2014.
  69. S. Motiian, M. Piccirilli, D.A. Adjeroh, G. Doretto, “Information bottleneck learning using privileged information for visual recognition,” in Proc., IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  70. M. Piccirrilli, G. Doretto, A. Ross,  D.A. Adjero, “A mobile structured light system for 3D face acquisition,” IEEE Sensors Journal, Feb. 2016.
  71. S.A. Rahman, D.A. Adjeroh, “Surface based body shape index and its relationship with all-cause mortality,” PLoS ONE, 10(12): 2015, e0144639.
  72. D. Cao, C. Chen, D.A. Adjeroh, A. Ross, “Predicting gender and weight from human metrology using a copula model,” in Proc., The 5th IEEE Int’l Conf. on Biometrics: Theory, Applications & Systems (BTAS), Sep. 2012.
  73. D. Cao, C. Chen, M. Piccirilli, D. Adjeroh, T. Bourlai, A. Ross, “Can facial metrology predict gender?,” in Proc., International Joint Conference on Biometrics (IJCB), Washington DC, Oct 11-13, 2011.
  74. E. Moore and T. Bourlai, "Expectation Maximization of Frequent Patterns - A Specific and Local Biclustering Algorithm for Biological Datasets", IEEE/ACM Transactions in Computational Biology and Bioinformatics, (Accepted), 2016.
  75. N. Narang and T. Bourlai, “Face recognition in the SWIR band when using single sensor multi-wavelength imaging systems”, Elsevier - Image and Vision Computing, vol. 33, pp. 26-43, Jan 2015 [IF 2.095].
  76. C. Whitelam and T. Bourlai, “Accurate Eye Localization in the Short Waved Infrared Spectrum through Summation Range Filters”, Elsevier – CVIU, vol. 139, pp. 59-72, October 2015 [IF 2.293].
  77. N. Osia and T. Bourlai, “A Spectral Independent Approach for Physiological and Geometric Based Face Recognition in the Visible, Middle-Wave and Long-Wave Infrared Bands”, Image and Vision Computing, Journal - Elsevier, Nov. 2014.
  78. A. Abaza, M. A. Harrison, T. Bourlai and A. Ross, Design and Evaluation of Photometric Image Quality Measures for Effective Face Recognition, IET Biometrics, 2014.
  79. A.B. Holbert, H.P. Whitelam, L.J. Sooter, J.M. Dawson, and L.A. Hornak, “Hand Bacteria as an Identifier – A Biometric Evaluation,” Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 4, no. 1, (2015).
  80. A. Kadiyala, K. Lee, L. E. Rodak, L.A. Hornak, D. Korakakis, and J.M. Dawson, “Improvement in the Light Extraction of Blue InGaN/GaN-Based LEDs Using Patterned Metal Contacts,” Electron Devices Society, IEEE Journal of the, vol. 2, no. 2, pp. 16-22,(2014).
  81. B. Hamza, M. Srungarapu, A. Kadiyala, J. M. Dawson, and L. Hornak, “Photonic Crystal Bioensors,” in Biosensors Based on Nanomaterials and Nanodevices (Chapter 8), CRC Press (2013).
  82. E. Marasco, A. Ross, J. Dawson, T. Moroose and T. Ambrose, “Detecting STR peaks in degraded DNA samples”, in Proc. 4th International Conference on Bioinformatics and Computational Biology (BICoB) 2012, p. 1 (2012).
  83. B. Hamza, An. Kadiyala, L.A. Hornak, Y. Liu, and J. M. Dawson, “Direct Fabrication of Two-dimensional Photonic Crystal Structures in Silicon Using Positive and Negative Hydrogen Silsesquioxane (HSQ) Patterns,” Microelectronic Engineering, 91, p. 70 (2012).
  84. S. Motiian, M. Piccirilli, D. Adjeroh and G. Doretto. Information bottleneck learning using privileged information for visual recognition. In Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR), pages 1496-1505, 2016.
  85. M. Piccirilli, G. Doretto, A. Ross and D. Adjeroh. A mobile structured light system for 3D face acquisition. IEEE Sensors Journal, vol. 16, no. 7, 1854-1855, 2016.
  86. F. Siyahjani, R. Almohsen, S. Sabri, and G. Doretto. A Supervised Low-rank Method for Learning Invariant Subspaces. In Proc. of the IEEE International Conference on Computer Vision (ICCV), 2015.
  87. G. Doretto, T. Sebastian, P. Tu, and J. Rittscher. Appearance-based person reidentification in camera networks: problem overview and current approaches. Journal of Ambient Intelligence and Humanized Computing, 2:127–151, Springer Berlin / Heidelberg, 2011.
  88. X. Wang, G. Doretto, T. B. Sebastian, J. Rittscher, and P. H. Tu. Shape and appearance context modeling. In Proc. of the IEEE Int. Conference on Computer Vision (ICCV), pages 1–8, 2007.
  89. L. Wen, and G-D. Guo,  “A computational approach to body mass index prediction from face images,” Image and Vision Computing, vol. 31, no. 5, pages 392-400, 2013. (This work was reported by New Scientist and many other Scientific News)
  90. G-D. Guo, L. Wen, and S. Yan, “Face authentication with makeup changes,” IEEE Trans. Circuits and Systems for Video Technology, DOI:10.1109/TCSVT.2013.2280076.
  91. Y. Zhu, and G-D. Guo,  “A study on visible to infrared action recognition,” IEEE Signal Processing Letters, vol. 20, no. 9, pages 897-900, 2013.
  92. C. Zhang, and G-D. Guo, “Age estimation with expression changes using multiple aging subspaces,” In Proc. of the IEEE Sixth Int’l Conf. on Biometrics: Theory, Applications and Systems (BTAS), Washington DC, September 29, 2013.
  93. G-D. Guo and X. Wang, “A Study on Human Age Estimation under Facial Expression Changes,” IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2547-2553, 2012.
  94. W. Dong, G. Shi, Y. Ma, X. Li, “Image Restoration via Simultaneous Sparse Coding: Where Structured Sparsity Meets Gaussian Scale Mixture,” International Journal of Computer Vision vol. 114, no. 2-3, pp. 217-232, 2015.

  95. W. Dong, G. Li, G. Shi, X. Li, Y. Ma, "Low-Rank Tensor Approximation with Laplacian Scale Mixture Modeling for Multiframe Image Denoising," Proceedings of the IEEE International Conference on Computer Vision, pp. 442-449, 2015.
  96. W. Dong, X. Li, Y. Ma, G. Shi, "Image restoration via Bayesian structured sparse coding," IEEE International Conference on Image Processing (ICIP), pp. 4018-4022, 2014.
  97. W. Hu, G. Cheung, X. Li, "Graph-based joint denoising and super-resolution of generalized piecewise smooth images," IEEE International Conference on Image Processing (ICIP), pp. 2056-2060, 2014.
  98. W. Dong, G. Shi, X. Li, Y. Ma, F. Huang, "Compressive sensing via nonlocal low-rank regularization," IEEE Transactions on Image Processing, vol. 23, no. 8, pp. 3618-3632, 2014.
  99. C. Torres, V. Fragoso, S.D. Hammond, J.C. Fried, B.S. Manjunath, "Eye-CU: Sleep pose classification for healthcare using multimodal multiview data,"  IEEE Winter Conference on Applications of Computer Vision (WACV), 1-9, 2016.
  100. M. Turk, V. Fragoso, "Computer Vision for Mobile Augmented Reality," Mobile Cloud Visual Media Computing, 3-42, 2015.
  101. C. Sweeney, V. Fragoso, T. Höllerer, M. Turk, "gdls: A scalable solution to the generalized pose and scale problem," European Conference on Computer Vision, 16-31, 2014.
  102. V. Fragoso, G. Srivastava, A. Nagar, Z. Li, K. Park, M. Turk, "Cascade of Box (CABOX) Filters for Optimal Scale Space Approximation,"  IEEE Conference on Computer Vision and Pattern Recognition (CVPR-Workshop),  2013 
  103. V. Fragoso, P. Sen, S. Rodriguez, M. Turk, "EVSAC: Accelerating Hypotheses Generation by Modeling Matching Scores with Extreme Value Theory,"  IEEE ICCV, 2013
  104. D. Yadav, N. Kohli, P. Pandey, R. Singh, M. Vatsa, A. Noore, "Effect of illicit drug abuse on face recognition," IEEE Winter Conference on Applications of Computer Vision (WACV), 1-7, 2016.
  105. S. Soleymani, A. Noore, "Dynamically reconfigurable evolutionary multi-context robust cellular array design,"  vol. 2, no. 1, pp. 1-12, 2016.
  106. S. Bharadwaj, H. S. Bhatt, R. Singh, M. Vatsa, A. Noore, "QFuse: Online learning framework for adaptive biometric system," Pattern Recognition, vol. 48, no. 11, pp. 3428-3439, 2015.
  107. N. Kohli, D. Yadav, A. Noore, "Multiple Projective Dictionary Learning to Detect Plastic Surgery for Face Verification," IEEE Access, vol. 3, pp. 2572-2580, 2015.
  108. A. Noore, R. Singh, M. Vasta, "Fusion, Sensor Level," Encyclopedia of Biometrics, pp. 772-778, 2015.
  109.  J. Zuo*, N. A. SchmidV. Dorairaj*, and L. Hornak, “Off-Angle Iris Recognition Utilizing Global ICA,” Open Transactions on Information Processing, .vol.1, no. 2, August 2014

  110.  S. Talarico, N. A. Schmid, M. Alkhweldi*, and M. C. Valenti, “Distributed Estimation of a Parametric Field: Algorithms and Performance Analysis,” IEEE Trans. on Signal Processing, vol. 62, no. 5, pp. 1041-1053, 2014.

  111.  M. Fanaei, M. C. Valenti, A. Jamalipour, and N. A. Schmid, “Optimal Power Allocation for Distributed Blue Estimation with Linear Spatial Collaboration,” in Proc. of 2014 International Conf. on Acoustic, Speech and Signal Processing, 2014.

  112. M. Fanaei, M. C. Valenti, and N. A. Schmid, “Effects of Spatial Randomness on Locating a Point Source with Distributed Sensors,” in Proc. of 2014 ICC Workshop, 2014.

  113.  Z. Cao* and N. A. Schmid, “Matching Heterogeneous Periocular Regions: Short and Long Standoff Distances,” in IEEE ICIP 2014, Paris, France, October, 2014.

  114. Xiaodong LiHaoran Xie, Yangqiu Song, Shanfeng ZhuQing LiFu Lee Wang, "Does Summarization Help Stock Prediction? A News Impact Analysis," IEEE Intelligent Systems, vol. 30, no 3, pp. 26-34, 2015.

  115. Shixia LiuXiting Wang, Yangqiu Song, Baining Guo, "Evolutionary Bayesian Rose Trees," IEEE Trans. Knowl. Data Eng.,  vol. 27, no. 6, pp. 1533-1546,  2015.

  116. Yangqiu Song, Shixia LiuXueqing LiuHaixun Wang, "Automatic Taxonomy Construction from Keywords via Scalable Bayesian Rose Trees," IEEE Trans. Knowl. Data Eng., vol. 27, no. 7, pp. 1861-1874, 2015. 

  117. Fangzhao WuYongfeng Huang, Yangqiu Song, Shixia Liu, "Towards building a high-quality microblog-specific Chinese sentiment lexicon," Decision Support Systems 87, pp. 39-49, 2016.

  118. Fangzhao WuYongfeng Huang, Yangqiu Song, "Structured microblog sentiment classification via social context regularization." Neurocomputing,  vol. 175, pp. 599-609, 2016.

  119. Lia R. EmanuelJoel FischerWendy Ju, Saiph Savage, "Innovations in autonomous systems: Challenges and opportunities for human-agent collaboration," CSCW Companion pp.193-196, 2016.

  120. Claudia Flores-Saviaga, Saiph Savage, Dario TaraborelliLeadWise: Using Online Bots to Recruite and Guide Expert Volunteers. CSCW Companion 2016: pp. 257-260, 2016.

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