Bowyer, K.W.[Kevin W.]
* 1987: Aspect Graphs for Convex Planar-Face Objects
* 1988: Aspect Graphs and Nonlinear Optimization in 3-D Object Recognition
* 1988: Creating the Perspective Projection Aspect Graph of Polyhedral Objects
* 1988: ERRORS-2: A 3-D Object Recognition System Using Aspect Graphs
* 1989: Computing the Orthographic Projection Aspect Graph of Solids of Revolution
* 1989: Developing the Aspect Graph Representation for Use in Image Understanding
* 1990: Aspect Graphs: an Introduction and Survey of Recent Results
* 1990: Computing the Orthographic Projection Aspect Graph of Solids of Revolution
* 1990: Computing the Visual Potential of an Articulated Assembly of Parts
* 1990: Direct Construction of the Perspective Projection Aspect Graph of Convex Polyhedra
* 1991: Achieving Generalized Object Recognition Through Reasoning About Association of Function to Structure
* 1991: Generalizing the Aspect Graph Concept to Include Articulated Assemblies
* 1991: Generic Recognition Through Qualitative Reasoning about 3-D Shape and Object Function
* 1991: Perspective Projection Aspect Graphs of Solids of Revolution: An Implementation
* 1991: Revolutions and Experimental Computer Vision
* 1991: Why Aspect Graphs Are Not (Yet) Practical for Computer Vision
* 1992: Applications of Artificial Intelligence X: Machine Vision and Robotics
* 1992: Indexing Function-Based Categories for Generic Recognition
* 1992: Scale Space Aspect Graph, The
* 1992: Special Issue on Directions in CAD-Based Vision
* 1992: Why Aspect Graphs Are Not (Yet) Practical for Computer Vision
* 1993: Active Robot Vision
* 1993: Computing the Generalized Aspect Graph for Objects with Moving Parts
* 1993: Computing the Perspective Projection Aspect Graph of Solids of Revolution
* 1993: Function-Based Generic Recognition for Multiple Object Categories
* 1993: Function-Based Recognition from Incomplete Knowledge of Object Shape
* 1993: Function-Based Recognition from Incomplete Knowledge of Shape
* 1993: Learning Combination of Evidence Functions in Object Recognition
* 1993: Methods for Combination of Evidence in Function-Based 3-D Object Recognition
* 1993: Scale Space Aspect Graph, The
* 1993: Special Issue on Active Robot Vision: Camera Heads, Model Based Navigation And Reactive Control
* 1993: Special Issue: State of the Art in Digital Mammographic Image Analysis
* 1993: Using Hyperquadrics for Shape Recovery from Range Data
* 1994: Function-Based Generic Recognition for Multiple Object Categories
* 1994: Generic Recognition of Articulated Objects by Reasoning about Functionality
* 1994: GRUFF-3: Generalizing the Domain of a Function-Based Recognition System
* 1994: Methodology for Evaluating Range Image Segmentation Techniques, A
* 1994: Report of the AAAI Fall Symposium on Machine Learning and Computer Vision: What, Why and How?
* 1994: Three-Dimensional Shape Representation
* 1995: Aspect Graphs and Their Use in Object Recognition
* 1995: Extracting a Valid Boundary Representation from a Segmented Range Image
* 1995: Generic Recognition of Articulated Objects Through Reasoning about Potential Function
* 1995: Learning Membership Functions in a Function-Based Object Recognition System
* 1995: Methodology for Evaluating Edge Detection Techniques for Range Images, A
* 1995: On Recovering Hyperquadrics from Range Data
* 1995: Point Correspondence in Unstructured Nonrigid Motion
* 1995: Range Image Segmentation: The User's Dilemma
* 1996: Advances in Image Understanding: A Festschrift for Azriel Rosenfeld
* 1996: Combination of Multiple Classifiers Using Local Accuracy Estimates
* 1996: Comparison of Edge Detectors: A Methodology and Initial Study
* 1996: Education for Computer Vision: Panel
* 1996: Experimental Comparison of Range Image Segmentation Algorithms, An
* 1996: Generic Object Recognition Using Form and Function
* 1996: Recognizing Object Function Through Reasoning about Partial Shape Descriptions and Dynamic Physical-Properties
* 1997: Combination of Multiple Classifiers Using Local Accuracy Estimates
* 1997: Generating ROC curves for artificial neural networks
* 1997: Robust Visual Method for Assessing the Relative Performance of Edge Detection Algorithms, A
* 1998: Are Edges Sufficient for Object Recognition
* 1998: Comparing Curved-Surface Range Image Segmenters
* 1998: Comparison of Edge Detectors
* 1998: Dynamic-Scale Model Construction From Range Imagery
* 1998: effect of edge strength on object recognition from edge images, The
* 1998: Empirical Evaluation Techniques in Computer Vision
* 1998: Evaluation of Edge Detection Algorithms Using a Structure from Motion Task
* 1998: Function from Visual Analysis and Physical Interaction: A Methodology for Recognition of Generic Classes of Objects
* 1998: Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task, An
* 1998: Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task, An
* 1998: Objective Comparison Methodology of Edge Detection Algorithms Using a Structure from Motion Task, An
* 1998: Objective Evaluation of Edge Detectors Using a Formally Defined Framework
* 1998: Objective Evaluation of Edge Detectors Using a Formally Defined Framework
* 1998: Overview of Work in Empirical Evaluation of Computer Vision Algorithms
* 1998: Preface to Image Computation and Education
* 1998: ROC curve evaluation of edge detector performance
* 1998: Space Envelope: A Representation for 3D Scenes, The
* 1999: Comparison of Edge Detectors Using an Object Recognition Task
* 1999: Edge Detector Evaluation Using Empirical ROC Curves
* 1999: Evaluation of Texture Segmentation Algorithms
* 1999: Introduction to the Special Section on Empirical Evaluation of Computer Vision Algorithms
* 2000: 20th Anniversary of the IEEE Transactions on Pattern Analysis and Machine Intelligence, The
* 2000: 20th Anniversary Survey: Introduction to Content-Based Image Retrieval at the End of the Early Years, A
* 2000: Automated Performance Evaluation of Range Image Segmentation
* 2000: Progress in Automated Evaluation of Curved Surface Range Image Segmentation
* 2000: Some Further Results of Experimental Comparison of Range Image Segmentation Algorithms
* 2001: Bagging Is a Small-Data-Set Phenomenon
* 2001: Comparison of Edge Detection Algorithms Using a Structure from Motion Task
* 2001: Comparison of Edge Detector Performance through Use in an Object Recognition Task
* 2001: Edge Detector Evaluation Using Empirical ROC Curves
* 2002: Advances in image processing and understanding: A festschrift for Thomas S. Huang
* 2002: Baseline results for the challenge problem of human ID using gait analysis
* 2002: evaluation of face and ear biometrics, An
* 2002: gait identification challenge problem: data sets and baseline algorithm, The
* 2003: Assessment of Time Dependency in Face Recognition: An Initial Study
* 2003: Comparison and combination of ear and face images in appearance-based biometrics
* 2003: Distributed learning with bagging-like performance
* 2003: Egomotion estimation of a range camera using the space envelope
* 2003: Multi-modal 2D and 3D biometrics for face recognition
* 2003: PCA-based face recognition in infrared imagery: baseline and comparative studies
* 2004: Automated Performance Evaluation of Range Image Segmentation Algorithms
* 2004: Multi-biometrics using facial appearance, shape and temperature
* 2004: survey of approaches to three-dimensional face recognition, A
* 2005: Adaptive Rigid Multi-region Selection for Handling Expression Variation in 3D Face Recognition
* 2005: Ear Biometrics Using 2D and 3D Images
* 2005: Empirical Evaluation of Advanced Ear Biometrics
* 2005: Evaluation of Multimodal 2D+3D Face Biometrics, An
* 2005: Experimental Evaluation of Iris Recognition
* 2005: Eye Perturbation Approach for Robust Recognition of Inaccurately Aligned Faces
* 2005: HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis, The
* 2005: Improved range image segmentation by analyzing surface fit patterns
* 2005: IR and visible light face recognition
* 2005: Multi-biometrics 2D and 3D Ear Recognition
* 2005: Overview of the Face Recognition Grand Challenge
* 2005: Random Subspaces and Subsampling for 2-D Face Recognition
* 2005: survey of approaches and challenges in 3D and multi-modal 3D-2D face recognition, A
* 2006: 3D Face Recognition with Region Committee Voting
* 2006: Automatic 3D Ear Recognition System, An
* 2006: Comparison of 3D Biometric Modalities, A
* 2006: Face Recognition Using 2-D, 3-D, and Infrared: Is Multimodal Better Than Multisample?
* 2006: Fusion of Infrared and Range Data: Multi-modal Face Images
* 2006: Human Face Modeling and Recognition Through Multi-View High Resolution Stereopsis
* 2006: Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression
* 2006: Preliminary Face Recognition Grand Challenge Results
* 2007: All Iris Code Bits are Not Created Equal
* 2007: Biometric Recognition Using 3D Ear Shape
* 2007: Comments on the CASIA version 1.0 Iris Data Set
* 2007: Comparison of Decision Tree Ensemble Creation Techniques, A
* 2007: fast algorithm for ICP-based 3D shape biometrics, A
* 2007: Learning to predict gender from iris images
* 2007: Multi-frame Approaches To Improve Face Recognition
* 2007: Multi-Modal Biometrics Involving the Human Ear
* 2007: Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition
* 2008: All Iris Filters are Not Created Equal
* 2008: Detecting and ordering salient regions for efficient browsing
* 2008: Detection of Iris Texture Distortions By Analyzing Iris Code Matching Results
* 2008: Image Understanding for Iris Biometrics: A survey
* 2008: Importance of Small Pupils: A Study of How Pupil Dilation Affects Iris Biometrics, The
* 2008: Iris Challenge Evaluation 2005, The
* 2008: Multi-Factor Approach to Improving Recognition Performance in Surveillance-Quality Video
* 2008: Profile Face Detection: A Subset Multi-Biometric Approach
* 2008: Rotated Profile Signatures for robust 3D feature detection
* 2008: Semi-supervised learning on large complex simulations
* 2008: Using multi-instance enrollment to improve performance of 3D face recognition
* 2009: Best Bits in an Iris Code, The
* 2009: Contact lenses: Handle with care for iris recognition
* 2009: Empirical Evidence for Correct Iris Match Score Degradation with Increased Time-Lapse between Gallery and Probe Matches
* 2009: Image Averaging for Improved Iris Recognition
* 2009: Introduction to the Special Section of Best Papers From the 2007 Biometrics: Theory, Applications, and Systems Conference
* 2009: Overview of the Multiple Biometrics Grand Challenge
* 2009: Pupil dilation degrades iris biometric performance
* 2009: Using fragile bit coincidence to improve iris recognition
* 2010: Degradation of iris recognition performance due to non-cosmetic prescription contact lenses
* 2010: FRVT 2006 and ICE 2006 Large-Scale Experimental Results
* 2010: Introduction to the Special Issue on Recent Advances in Biometrics
* 2010: Similarity of iris texture between identical twins
* 2011: 3D Twins and Expression Challenge
* 2011: cross-sensor evaluation of three commercial iris cameras for iris biometrics, A
* 2011: Detecting questionable observers using face track clustering
* 2011: Distinguishing identical twins by face recognition
* 2011: Experimental evidence of a template aging effect in iris biometrics
* 2011: Genetically identical irises have texture similarity that is not detected by iris biometrics
* 2011: Improved Iris Recognition through Fusion of Hamming Distance and Fragile Bit Distance
* 2011: Pose-Robust Recognition of Low-Resolution Face Images
* 2011: Predicting performance of face recognition systems: An image characterization approach
* 2011: Useful features for human verification in near-infrared periocular images
* 2012: Accuracy of Iris Recognition Systems Degrades with Increase in Elapsed Time
* 2012: Analysis of template aging in iris biometrics
* 2012: Best of Automatic Face and Gesture Recognition 2011
* 2012: Color balancing for change detection in multitemporal images
* 2012: Effects of dominance and laterality on iris recognition
* 2012: Multidimensional Scaling for Matching Low-Resolution Face Images
* 2012: Predicting good, bad and ugly match Pairs
* 2012: results of the NICE.II Iris biometrics competition, The
* 2012: sparse representation approach to face matching across plastic surgery, A
* 2013: Handbook of Iris Recognition
* 2013: Pose-Robust Recognition of Low-Resolution Face Images
* 2014: Active Clustering with Ensembles for Social structure extraction
* 2014: Automated Poststorm Damage Classification of Low-Rise Building Roofing Systems Using High-Resolution Aerial Imagery
* 2014: Cosmetic Contact Lenses and Iris Recognition Spoofing
* 2014: effectiveness of face detection algorithms in unconstrained crowd scenes, The
* 2014: Gender Classification from Iris Images Using Fusion of Uniform Local Binary Patterns
* 2014: Recognition of Facial Attributes Using Adaptive Sparse Representations of Random Patches
* 2015: Automatic facial attribute analysis via adaptive sparse representation of random patches
* 2015: Critical examination of the IREX VI results
* 2015: Exploratory analysis of an operational iris recognition dataset from a CBSA border-crossing application
* 2015: Face recognition under pose variation with active shape model to adjust Gabor filter kernels and to correct feature extraction location
* 2015: Face recognition under pose variation with local Gabor features enhanced by Active Shape and Statistical Models
* 2015: Lessons from collecting a million biometric samples
* 2016: analysis of 1-to-first matching in iris recognition, An
* 2016: Dilation-aware enrolment for iris recognition
* 2016: Recognizing Future Hot Topics and Hard Problems In Biometrics Research
* 2017: Gender-from-Iris or Gender-from-Mascara?
* 2017: Lessons from collecting a million biometric samples
* 2017: Provenance filtering for multimedia phylogeny
* 2017: Spotting the difference: Context retrieval and analysis for improved forgery detection and localization
* 2017: U-Phylogeny: Undirected provenance graph construction in the wild
* 2018: Analysis of diurnal changes in pupil dilation and eyelid aperture
* 2018: Found a Good Match: Should I Keep Searching?: Accuracy and Performance in Iris Matching Using 1-to-First Search
* 2018: Image Provenance Analysis at Scale
* 2018: Presentation Attack Detection for Iris Recognition: An Assessment of the State-of-the-Art
* 2019: Beyond Pixels: Image Provenance Analysis Leveraging Metadata
* 2019: Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
* 2019: Fast Face Image Synthesis With Minimal Training
* 2019: Iris Presentation Attack Detection Based on Photometric Stereo Features
* 2019: Iris Recognition: Comparing Visible-Light Lateral and Frontal Illumination to NIR Frontal Illumination
* 2019: Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-Driven Verification
* 2019: Predicting Gender From Iris Texture May Be Harder Than It Seems
* 2020: Analysis of Gender Inequality In Face Recognition Accuracy
* 2020: Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No
* 2020: Iris presentation attack detection: Where are we now?
* 2020: On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs
* 2021: Fast Local Spatial Verification for Feature-Agnostic Large-Scale Image Retrieval
* 2021: Study of the Human Perception of Synthetic Faces, A
* 2021: Unifying frame rate and temporal dilations for improved remote pulse detection
* 2022: Analysis of Manual and Automated Skin Tone Assignments
* 2022: Digital and Physical-World Attacks on Remote Pulse Detection
* 2022: Human-Aided Saliency Maps Improve Generalization of Deep Learning
* 2022: Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition
* 2022: Remote Pulse Estimation in the Presence of Face Masks
* 2023: Analyzing the Impact of Gender Misclassification on Face Recognition Accuracy
* 2023: Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks
* 2023: CAST: Conditional Attribute Subsampling Toolkit for Fine-grained Evaluation
* 2023: Consistency and Accuracy of CelebA Attribute Values
* 2023: CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning-Based Synthetic Face Detection
* 2023: Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
* 2023: Gender Gap in Face Recognition Accuracy Is a Hairy Problem, The
* 2023: Human Saliency-Driven Patch-based Matching for Interpretable Post-Mortem Iris Recognition
* 2023: Logical Consistency and Greater Descriptive Power for Facial Hair Attribute Learning
* 2024: Facial Hair Area in Face Recognition Across Demographics: Small Size, Big Effect
* 2024: Impact of Blur and Resolution on Demographic Disparities in 1-to-Many Facial Identification
Includes: Bowyer, K.W.[Kevin W.] Bowyer, K.W.
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