Person Recognition in Personal Photo Collections
arXiv:1710.03224 · doi:10.1109/TPAMI.2018.2877588
Abstract
People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation. For human identification task, however, traditional focus of computer vision has been face recognition and pedestrian re-identification. Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e.g. backward viewpoints, unusual poses) and great changes in appearance. To tackle this problem, we build a simple person recognition framework that leverages convnet features from multiple image regions (head, body, etc.). We propose new recognition scenarios that focus on the time and appearance gap between training and testing samples. We present an in-depth analysis of the importance of different features according to time and viewpoint generalisability. In the process, we verify that our simple approach achieves the state of the art result on the PIPA benchmark, arguably the largest social media based benchmark for person recognition to date with diverse poses, viewpoints, social groups, and events. Compared the conference version of the paper, this paper additionally presents (1) analysis of a face recogniser (DeepID2+), (2) new method naeil2 that combines the conference version method naeil and DeepID2+ to achieve state of the art results even compared to post-conference works, (3) discussion of related work since the conference version, (4) additional analysis including the head viewpoint-wise breakdown of performance, and (5) results on the open-world setup.
arXiv comment18 pages, 20 figures; to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence
References in corpus (13)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- FaceNet: A Unified Embedding for Face Recognition and Clustering
- Learning Face Representation from Scratch
- Additive Margin Softmax for Face Verification
- L2-constrained Softmax Loss for Discriminative Face Verification
- Looking Beyond Appearances: Synthetic Training Data for Deep CNNs in Re-identification
- Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking
- Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?
- Triplet Probabilistic Embedding for Face Verification and Clustering
- Robust Depth-based Person Re-identification
- Deep Metric Learning for Practical Person Re-Identification
- Rethinking Feature Discrimination and Polymerization for Large-scale Recognition
- VIP: Finding Important People in Images