Report on the BTAS 2016 Video Person Recognition Evaluation

© 2016 IEEE. This report presents results from the Video Person Recognition Evaluation held in conjunction with the 8th IEEE International Conference on Biometrics: Theory, Applications, and Systems (BTAS). Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1,401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. An additional experiment required algorithms to recognize people in videos from the Video Database of Moving Faces and People (VDMFP). There were 958 videos in this experiment of 297 subjects. Four groups from around the world participated in the evaluation. The top verification rate for PaSC from this evaluation is 0.98 at a false accept rate of 0.01 - a remarkable advancement in performance from the competition held at FG 2015

Learning Granularity-Unified Representations for Text-to-Image Person Re-identification

Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps. Existing works usually ignore the difference in feature granularity between the two modalities, i.e., the visual features are usually fine-grained while textual features are coarse, which is mainly responsible for the large inter-modal gaps. In this paper, we propose an end-to-end framework based on transformers to learn granularity-unified representations for both modalities, denoted as LGUR. LGUR framework contains two modules: a Dictionary-based Granularity Alignment (DGA) module and a Prototype-based Granularity Unification (PGU) module. In DGA, in order to align the granularities of two modalities, we introduce a Multi-modality Shared Dictionary (MSD) to reconstruct both visual and textual features. Besides, DGA has two important factors, i.e., the cross-modality guidance and the foreground-centric reconstruction, to facilitate the optimization of MSD. In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance. Comprehensive experiments show that our LGUR consistently outperforms state-of-the-arts by large margins on both CUHK-PEDES and ICFG-PEDES datasets. Code will be released at https://github.com/ZhiyinShao-H/LGUR.Comment: Accepted by ACM Multimedia 202

Automated Cleaning of Identity Label Noise in A Large-scale Face Dataset Using A Face Image Quality Control

For face recognition, some very large-scale datasets are publicly available in recent years which are usually collected from the internet using search engines, and thus have many faces with wrong identity labels (outliers). Additionally, the face images in these datasets have different qualities. Since the low quality face images are hard to identify, current automated identity label cleaning methods are not able to detect the identity label error in the low quality faces. Therefore, we propose a novel approach for cleaning the identity label error more low quality faces. Our face identity labels cleaned by our method can train better models for low quality face recognition. The problem of low quality face recognition is very common in the real-life scenarios, where face images are usually captured by surveillance cameras in unconstrained conditions. \\ \\ Our proposed method starts by defining a clean subset for each identity consists of top high-quality face images and top search ranked faces that has the identity label. We call this set the ``identity reference set\u27\u27. After that, a ``quality adaptive similarity threshold\u27\u27 is applied to decide on whether a face image from the original identity set is similar to the identity reference set (inlier) or not. The quality adaptive similarity threshold means using adaptive threshold values for faces based on their quality scores. Because the inlier low quality faces have less facial information and are likely to achieve less similarity score to the identity reference than the high-quality inlier faces, using less strict threshold to classify low quality faces saves them from being falsely classified as outlier. \\ \\ In our low-to-high-quality face verification experiments, the deep model trained on our cleaning results of MS-Celeb-1M.v1 outperforms the same model trained using MS-Celeb-1M.v1 cleaned by the semantic bootstrapping method. We also apply our identity label cleaning method on a subset of the CACD face dataset, our quality based cleaning can deliver a higher precision and recall than a previous method