	D. Maltoni, D. Maio, A. K. Jain, J. Feng, Handbook of Fingerprint Recognition (Third Edition), Springer, 2022.
	2D fingerprint pose estimation Estimation of fingerprint pose has applications in fingerprint feature extraction, quality estimation, matching, and indexing. Y. Duan, J. Feng, J. Lu, J. Zhou: "Estimating Fingerprint Pose via Dense Voting". IEEE Transactions on Information Forensics and Security, 18: 2493-2507 (2023) Q. Yin, J. Feng, J. Lu, J. Zhou: "Joint Estimation of Pose and Singular Points of Fingerprints". IEEE Transactions on Information Forensics and Security, 16: 1467-1479 (2021) S. Gu, J. Feng, J. Lu, J. Zhou: "Efficient Rectification of Distorted Fingerprints". IEEE Transactions on Information Forensics and Security, 13(1): 156-169 (2018) J. Ouyang, J. Feng, J. Lu, Z. Guo, J. Zhou: "Fingerprint pose estimation based on faster R-CNN". IJCB 2017: 268-276. (Best Student Paper Award Runner-Up) Y. Su, J. Feng, J. Zhou: "Fingerprint indexing with pose constraint". Pattern Recognition, 54: 1-13 (2016) X. Yang, J. Feng, J. Zhou: "Localized dictionaries based orientation field estimation for latent fingerprints". IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5): 955-969 (2014)
	3D fingerprint reconstruction 3D fingerprint is the original form of fingerprint, containing the most complete feature information. We explore how to use low-cost sensors (such as, a single camera, various touch-based fingerprint sensors) to capture 3D fingerprints. Z. Cui, J. Feng, J. Zhou: "Monocular 3D Fingerprint Reconstruction and Unwarping". IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(7): 8679-8695 (2023) Y. Duan, K. He, J. Feng, J. Lu, J. Zhou: "Estimating 3D Finger Pose via 2D-3D Fingerprint Matching". IUI 2022: 459-469. (Best Paper Award Honorable Mention)
	Rectification of distorted fingerprint Recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. X. Guan, Y. Duan, J. Feng, J. Zhou: "Regression of Dense Distortion Field from a Single Fingerprint Image". IEEE Transactions on Information Forensics and Security, 18: 4377-4390 (2023) X. Guan, Y. Duan, J. Feng, J. Zhou, "Direct Regression of Distortion Field from a Single Fingerprint Image". IJCB 2022: 1-8 S. Gu, J. Feng, J. Lu, J. Zhou: "Efficient Rectification of Distorted Fingerprints". IEEE Transactions on Information Forensics and Security, 13(1): 156-169 (2018) X. Si, J. Feng, J. Zhou, Y. Luo: "Detection and rectification of distorted fingerprints". IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3): 555-568 (2015) [data]
	Latent fingerprint feature extraction Automatic feature extraction in latent fingerprints is extremely challenging due to poor quality of most latents, such as unclear ridge structures, overlapped lines and letters, and overlapped fingerprints. Y. Duan, J. Feng, J. Lu, J. Zhou: "Orientation Field Estimation for Latent Fingerprints with Prior Knowledge of Fingerprint Pattern". IJCB 2021: 1-8 Q. Yin, J. Feng, J. Lu, J. Zhou: "Orientation Field Estimation for Latent Fingerprints by Exhaustive Search of Large Database". BTAS 2018: 1-9 M. Xu, J. Feng, J. Lu, J. Zhou: "Latent fingerprint enhancement using Gabor and minutia dictionaries". ICIP 2017: 3540-3544 C. Chen, J. Feng, J. Zhou: "Multi-scale dictionaries based fingerprint orientation field estimation". ICB 2016: 1-8 X. Yang, J. Feng, J. Zhou, S. Xia: "Detection and segmentation of latent fingerprints". WIFS 2015: 1-6 X. Si, J. Feng, J. Zhou: "Enhancing latent fingerprints on banknotes". IJCB 2014: 1-8 X. Yang, J. Feng, J. Zhou: "Localized dictionaries based orientation field estimation for latent fingerprints". IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(5): 955-969 (2014) J. Feng, J. Zhou, A.K. Jain: "Orientation field estimation for latent fingerprint enhancement". IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4): 925-940 (2013) [data] S. Yoon, J. Feng, A.K. Jain: "Latent fingerprint enhancement via robust orientation field estimation". IJCB 2011: 1-8 (Best Student Paper Award)
	Separating overlapped fingerprints Overlapped fingerprints are not unusual in latent fingerprints taken from crime scenes. However, separating overlapped fingerprints into individual fingerprints is a very challenging problem for both existing automatic fingerprint recognition systems and human fingerprint experts. We proposed algorithms for separating overlapped fingerprints. J. Feng, Y. Shi, J. Zhou: "Robust and efficient algorithms for separating latent overlapped fingerprints". IEEE Transactions on Information Forensics and Security, 7(5): 1498-1510 (2012)[data] F. Chen, J. Feng, A.K. Jain, J. Zhou, J. Zhang: "Separating overlapped fingerprints". IEEE Transactions on Information Forensics and Security, 6(2): 346-359 (2011) [data]
	Fingerprint quality assessment Fingerprint quality assessment is important for capturing high quality fingerprints and detecting uncooperative behaviors. We propose several algorithms to detect low quality fingerprints caused by different factors. Y. Su, J. Feng, J. Zhou: "A video surveillance system for fingerprint acquisition". 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore, July 18-20, 2012. (Best Paper Award) S. Yoon, J. Feng, A.K. Jain: "Altered fingerprints: analysis and detection". IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3): 451-464 (2012)
	Fingerprint reconstruction from minutiae The compactness of minutiae representation has created an impression that minutiae does not contain sufficient information to allow the reconstruction of the original fingerprint image. This opinion is challenged by the proposed fingerprint reconstruction technique. The proposed technique also has other uses, such as reconstructing latent prints using minutiae marked by human experts, and improving the performance of fingerprint recognition systems with limitted storage space. J. Feng, A.K. Jain: "Fingerprint reconstruction: from minutiae to phase". IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(2): 209-223 (2011)
	Dense registration of fingerprints Dense registration is useful for fingerprint matching and mosaicking. X. Guan, J. Feng, J. Zhou: "Phase-aggregated Dual-branch Network for Efficient Fingerprint Dense Registration". IEEE Transactions on Information Forensics and Security, 19: 5712-5724 (2024) Z. Cui, J. Feng, J. Zhou: "Dense Registration and Mosaicking of Fingerprints by Training an End-to-End Network". IEEE Transactions on Information Forensics and Security, 16: 627-642 (2021) Z. Cui, J. Feng, J. Zhou: "Dense Fingerprint Registration via Displacement Regression Network". ICB 2019: 1-8 Z. Cui, J. Feng, S. Li, J. Lu, J. Zhou: "2-D Phase Demodulation for Deformable Fingerprint Registration". IEEE Transactions on Information Forensics and Security, 13(12): 3153-3165 (2018) X. Si, J. Feng, B. Yuan, J. Zhou: "Dense registration of fingerprints". Pattern Recognition, 63: 87-101 (2017)
	Latent fingerprint matching Latent fingerprint identification is of critical importance for identifying suspects. Poor quality of ridge impressions, small finger area and large non-linear distortion are the main difficulties in latent fingerprint matching, compared to plain or rolled fingerprint matching. S. Gu, J. Feng, J. Lu, J. Zhou: "Latent Fingerprint Registration via Matching Densely Sampled Points". IEEE Transactions on Information Forensics and Security, 16: 1231-1244 (2021) A. Paulino, J. Feng, A.K. Jain: "Latent fingerprint matching using descriptor-based Hough transform". IEEE Transactions on Information Forensics and Security, v8(1): 31-45 (2013) A.K. Jain, J. Feng: "Latent fingerprint matching". IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(1): 88-100 (2011) A. Paulino, A.K. Jain, J. Feng: "Latent fingerprint matching: Fusion of manually marked and derived minutiae". SIBGRAPI, Brazil, Aug. 2010. ( Best Student Paper Award) J. Feng, S. Yoon, A.K. Jain: "Latent fingerprint matching: fusion of rolled and plain fingerprints". International Conference on Biometrics (ICB), pp. 695-704, 2009. A.K. Jain, J. Feng, A. Nagar, K. Nandakumar: "On matching latent fingerprint". CVPR Workshop on Biometrics, pp. 1-8, 2008.
	Latent palmprint matching Latent palmprint matching is a challenging problem because latent prints lifted at crime scenes are of poor image quality, cover only a small area of the palm, have a complex background and many creases. J. Dai, J. Feng, J. Zhou: "Robust and efficient ridge-based palmprint matching". IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(8): 1618-1632 (2012) [data] X. Yang, J. Feng, J. Zhou: "Palmprint indexing based on ridge features". IJCB, Washington, DC, Oct. 11-13, 2011. A.K. Jain, J. Feng: "Latent palmprint matching". IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(6): 1032-1047 (2009)
	Ridge skeleton matching The most popular fingerprint representation is based on minutiae points, which is a compact and lossy representation of ridge skeleton, the Level 2 representation of fingerprints. Here we explore matching ridge skeletons directly for the purpose of completely utilizing the discriminating power of Level 2 features in fingerprints. J. Feng, Z. Ouyang, A. Cai: "Fingerprint matching using ridges". Pattern Recognition, 39(11): 2131-2140 (2006) J. Feng, A. Cai: "Fingerprint indexing using ridge invariants". International Conference on Pattern Recognition, vol. 4, pp. 433-436, 2006. J. Feng, A. Cai: "Fingerprint representation and matching in ridge coordinate system". International Conference on Pattern Recognition, vol. 4, pp. 485-488, 2006.
	Global descriptors Z. Pan, Y. Duan, J. Feng, J. Zhou: "Fixed-length Dense Descriptor for Efffcient Fingerprint Matching". IEEE International Workshop on Information Forensics and Security (WIFS) 2024: 1-6 S. Gu, J. Feng, J. Lu, J. Zhou: "Latent Fingerprint Indexing: Robust Representation and Adaptive Candidate List". IEEE Transactions on Information Forensics and Security, 17: 908-923 (2022) Y. Luo, J. Feng, J. Zhou: "Fingerprint matching based on global minutia cylinder code". IJCB 2014: 1-8
	Minutia descriptors Establishing minutiae correspondence between two fingerprints is difficult due to unknown alignment, nonlinear deformation, noise, and occlusion. Designing robust minutia descriptors is a way to facilitate this problem. Z. Pan, Y. Duan, X. Guan, J. Feng, J. Zhou: "Latent Fingerprint Matching via Dense Minutia Descriptor". IJCB 2024. J. Feng, J. Zhou: "A performance evaluation of fingerprint minutia descriptors". International Conference on Hand-based Biometrics (ICHB), Hong Kong, 2011. J. Feng: "Combining minutiae descriptors for fingerprint matching". Pattern Recognition, 41(1): 342-352 (2008)
	Statistical modeling of friction ridge patterns Statistical modeling of friction ridge patterns is a fundamental problem and can be used in both synthesis and analysis of friction ridge patterns. J. Feng, H. Zhou, J. Zhou: "A preliminary study of handprint synthesis". 6th Chinese Conference on Biometric Recognition (CCBR), pp. 133-140, Beijing, 2011. J. Feng, A.K. Jain, J. Zhou: "Statistical modeling of fingerprint minutiae". Intelligent Vision Group at Tsinghua University, Technical Report, THU-IVG-TR-2011-1, Jan. 2011.

	3D finger angle estimation Estimation of 3D finger angle (orientation) can be used in human computer interaction. C. Li, J. Yu, K. He, J. Feng, J. Zhou: "SwivelTouch: Boosting Touchscreen Input with 3D Finger Rotation Gesture". Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 8(2): 53:1-53:30 (2024) [video] K. He, C. Li, Y. Duan, J. Feng, J. Zhou: "TrackPose: Towards Stable and User Adaptive Finger Pose Estimation on Capacitive Touchscreens". Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 7(4): 161:1-161:22 (2023) [video] Y. Duan, J. Yu, J. Feng, K. He, J. Lu, J. Zhou: "3D Finger Rotation Estimation from Fingerprint Images". Proc. ACM on Human-Computer Interaction (PACMHCI), 7(ISS): 114-134 (2023) [video] K. He, Y. Duan, J. Feng, J. Zhou: "Estimating 3D Finger Angle via Fingerprint Image". Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 6(1): 14:1-14:22 (2022) [video] Y. Duan, K. He, J. Feng, J. Lu, J. Zhou: "Estimating 3D Finger Pose via 2D-3D Fingerprint Matching". IUI 2022: 459-469. (Best Paper Award Honorable Mention)
	Text entry via fingerprint recognition By turning all fingers into a keyboard, PrinType can be used for text entry in VR/AR, text entry of blind people, and continuous authentication. Z. Liu, J. He, J. Feng, J. Zhou: "PrinType: Text Entry via Fingerprint Recognition". Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 6(4): 174:1-174:31 (2023) [video]
	Shear input via fingerprint deformation Lateral, longitudinal and rotational deformations are extracted from fingerprint images and mapped to 3DOF control commands. J. Yu, J. Feng, J. Zhou: "PrintShear: Shear Input Based on Fingerprint Deformation". Proc. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 7(2): 81:1-81:22 (2023)

	Vesssel segmentation Z. Guo, J. Feng, W. Lu, Y. Yin, G. Yang, J. Zhou: "Cross-modality Cerebrovascular Segmentation Based on Pseudo-label Generation via Paired Data". Computerized Medical Imaging and Graphics, 115:102393 (2024) Z. Guo, Z. Tan, J. Feng, J. Zhou: "3D Vascular Segmentation Supervised by 2D Annotation of Maximum Intensity Projection". IEEE Transactions on Medical Imaging (2024) Z. Guo, J. Feng, J. Zhou: "Edge-Aware Vessel Segmentation using Scribble Supervision". ISBI 2024 Z. Guo, J. Feng, W. Lu, Y. Yin, G. Yang, J. Zhou: "Training Cross-Modality Cerebrovascular Segmentation Networks with Paired Images". ISBI 2023 Z. Tan, J. Feng, J. Zhou: "SGNet: Structure-Aware Graph-Based Network for Airway Semantic Segmentation". MICCAI (1) 2021: 153-163 H. Fang, Z. Guo, G. Shao, Z. Tan, J. Yu, J. Liu, Y. Cao, J. Zhou, H. Shi, J. Feng: "Vessel Extraction and Analysis of Aortic Dissection". STACOM@MICCAI 2021: 48-56 Z. Guo, Y. Zhang, J. Feng, E. Yang, L. Qin, J. Zhou: "Coronary Artery Centerline Refinement Using GCN Trained with Synthetic Data". STACOM@MICCAI 2021: 21-28 Z. Li, J. Feng, Z. Feng, Y. An, Y. Gao, B. Lu, J. Zhou: "Lumen Segmentation of Aortic Dissection with Cascaded Convolutional Network". STACOM@MICCAI 2018: 122-130 Y. Duan, J. Feng, J. Lu, J. Zhou: "Context Aware 3D Fully Convolutional Networks for Coronary Artery Segmentation". STACOM@MICCAI 2018: 85-93
	Detection of vesssel landmarks Z. Tan, J. Feng, W. Lu, Y. Yin, G. Yang, J. Zhou: "Multi-task global optimization-based method for vascular landmark detection". Computerized Medical Imaging and Graphics, 114: 102364 (2024) Z. Tan, J. Feng, W. Lu, Y. Yin, G. Yang, J. Zhou: "Adversarial Learning Framwork for Cerebrovascular Landmark Detection Using Cross-modality Information". ISBI 2023 Z. Tan, J. Feng, W. Lu, Y. Yin, G. Yang, J. Zhou: "Cerebrovascular Landmark Detection under Anatomical Variations". ISBI 2022: 1-5 [data] Z. Tan, J. Feng, J. Zhou: "Multi-Task Learning Network for Landmark Detection in Anatomical Tree Structures". ISBI 2021: 1975-1979 Z. Tan, Y. Duan, Z. Wu, J. Feng, J. Zhou: "A Cascade Regression Model for Anatomical Landmark Detection". STACOM@MICCAI 2019: 43-51
	Vesssel registration S. Zeng, J. Feng, Y. An, B. Lu, J. Lu, J. Zhou: "Towards Accurate and Complete Registration of Coronary Arteries in CTA Images". MICCAI (2) 2018: 419-427 Y. Luo, J. Feng, M. Xu, J. Zhou, J.K. Min, G. Xiong: "Registration of coronary arteries in computed tomography angiography images using Hidden Markov Model". EMBC 2015: 1993-1996
	Diagnosis of pneumonia W. Chen, X. Han, J. Wang, Y. Cao, X. Jia, Y. Zheng, J. Zhou, W. Zeng, L. Wang, H. Shi, J. Feng: "Deep diagnostic agent forest (DDAF): A deep learning pathogen recognition system for pneumonia based on CT". Computers in Biology and Medicine, 141: 105143 (2022) W. Chen, J. Feng, J. Zhou: "TriageNet: A Multi-Agent Diagnosis Network for Imbalanced Data". ISBI 2022: 1-5 C. Jin, W. Chen, Y. Cao, Z. Xu, Z. Tan, X. Zhang, L. Deng, C. Zheng, J. Zhou, H. Shi, J. Feng: "Development and evaluation of an artificial intelligence system for COVID-19 diagnosis". Nature Communications, 11(1): 1-14 (2020)
	Segmentation of left atrial appendage C. Jin, J. Feng, L. Wang, H. Yu, J. Liu, J. Lu, J. Zhou: "Left Atrial Appendage Segmentation Using Fully Convolutional Neural Networks and Modified Three-Dimensional Conditional Random Fields". IEEE Journal of Biomedical and Health Informatics, 22(6): 1906-1916 (2018) C. Jin, J. Feng, L. Wang, H. Yu, J. Liu, J. Lu, J. Zhou: "Left atrial appendage segmentation and quantitative assisted diagnosis of atrial fibrillation based on fusion of temporal-spatial information". Computers in Biology and Medicine, 96: 52-68 (2018)