Recognizing Partial Biometric Patterns

Abstract

Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching. In this area, deep learning based methods are widely applied to match these partial captured objects caused by occlusions, variations of postures or just partial out of view in person re-identification and partial face recognition. However, most current methods are not able to identify an individual in case that some parts of the object are not obtainable, while the rest are specialized to certain constrained scenarios. To this end, we propose a robust general framework for arbitrary biometric matching scenarios without the limitations of alignment as well as the size of inputs. We introduce a feature post-processing step to handle the feature maps from FCN and a dictionary learning based Spatial Feature Reconstruction (SFR) to match different sized feature maps in this work. Moreover, the batch hard triplet loss function is applied to optimize the model. The applicability and effectiveness of the proposed method are demonstrated by the results from experiments on three person re-identification datasets (Market1501, CUHK03, DukeMTMC-reID), two partial person datasets (Partial REID and Partial iLIDS) and two partial face datasets (CASIA-NIR-Distance and Partial LFW), on which state-of-the-art performance is ensured in comparison with several state-of-the-art approaches. The code is released online and can be found on the website: https://github.com/lingxiao-he/Partial-Person-ReID.

arXiv comment13 pages, 11 figures

References in corpus (10)

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• Deep Residual Learning for Image Recognition
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• Rich feature hierarchies for accurate object detection and semantic segmentation
• AlignedReID: Surpassing Human-Level Performance in Person Re-Identification
• Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
• Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)
• Dual Attention Matching Network for Context-Aware Feature Sequence based Person Re-Identification
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Cited by in corpus (19)

• MHSA-Net: Multi-Head Self-Attention Network for Occluded Person Re-Identification
• Incomplete Descriptor Mining with Elastic Loss for Person Re-Identification
• Feature Completion Transformer for Occluded Person Re-identification
• High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Identification
• Relation-Aware Global Attention for Person Re-identification
• Identity-Guided Human Semantic Parsing for Person Re-Identification
• Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer
• Pose-guided Visible Part Matching for Occluded Person ReID
• Densely Semantically Aligned Person Re-Identification
• Neighbourhood-guided Feature Reconstruction for Occluded Person Re-Identification
• Semantics-Aligned Representation Learning for Person Re-identification
• STNReID : Deep Convolutional Networks with Pairwise Spatial Transformer Networks for Partial Person Re-identification
• Devil's in the Details: Aligning Visual Clues for Conditional Embedding in Person Re-Identification
• Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-identification
• ESA-ReID: Entropy-Based Semantic Feature Alignment for Person re-ID
• Beyond Triplet Loss: Meta Prototypical N-tuple Loss for Person Re-identification
• Feature Completion for Occluded Person Re-Identification
• Pose-Guided Feature Learning with Knowledge Distillation for Occluded Person Re-Identification
• Deep learning for identification and face, gender, expression recognition under constraints