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Human Identification at a Distance: Challenges, Methods and Results on HID 2023
Abstract
Human Identification at a Distance (HID) is an important research area due to its importance (especially in bio-metrics) and inherent challenges within this domain. To mitigate some of the constraints, we have introduced the HID challenge. This paper presents an overview of the 4th International Competition on Human Identification at a Distance (HID 2023), which serves as a benchmark for evaluating various methods in the field of human identification at a distance. We have introduced a new dataset, SUSTech-Competition, engulfing a cross-domain challenge. This dataset has 859 subjects, having various variations of clothing, carrying conditions, occlusions, and view angles. With a substantial participation of 254 registered teams, HID 2023 has attracted considerable attention and yielded highly encouraging results. Notably, the top-performing teams achieved significantly good accuracies. In this paper , we provide an introduction to the competition, encompassing the dataset, experimental settings, and competition organization, as well as an analysis of the results obtained by the top teams. Additionally, we delve into the methodolo-gies employed by these leading teams. The progress demonstrated in this competition offers an optimistic outlook on the advancements in gait recognition, highlighting its potential for robust real applications.
This paper describes the world’s largest gait database with wide view variation, the “OU-ISIR gait database, multi-view large population dataset (OU-MVLP)”, and its application to a statistically reliable performance evaluation of vision-based cross-view gait recognition. Specifically, we construct a gait dataset that includes 10,307 subjects (5114 males and 5193 females) from 14 view angles ranging 0° −90°, 180° −270°.
In addition, we evaluate various approaches to gait recognition which are robust against view angles. By using our dataset, we can fully exploit a state-of-the-art method requiring a large number of training samples, e.g., CNN-based cross-view gait recognition method, and we validate effectiveness of such a family of the methods.
When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a k-reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the k-reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, a k-reciprocal feature is calculated by encoding its k-reciprocal nearest neighbors into a single vector, which is used for re-ranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the large-scale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method.
We propose the simple and efficient method of semi-supervised learning for deep neural networks. Basically, the proposed network is trained in a supervised fashion with labeled and unlabeled data simultaneously. For un-labeled data, Pseudo-Label s, just picking up the class which has the maximum predicted probability, are used as if they were true labels. This is in effect equivalent to Entropy Regularization. It favors a low-density separation between classes, a commonly assumed prior for semi-supervised learning. With De-noising Auto-Encoder and Dropout, this simple method outperforms conventional methods for semi-supervised learning with very small labeled data on the MNIST handwritten digit dataset.
The paper provides a summary of the Competition on Human Identification at a Distance 2022 (HID 2022), which is the third one in a series of competitions. HID 2022 is for promoting the research in human identification at a distance by providing a benchmark to evaluate different methods. The competition attracted 112 valid registered teams. 71 teams and 51 teams submitted their results in the first phase and the second phase, respectively. Very encouraging results have been achieved, and the accuracies of the top teams are much higher than those achieved in the previous two competitions. In this paper, we introduce the competition including the dataset, experimental settings, competition organization, results from the top teams and their analysis. The methods used by the top teams are also presented in the paper. The progress of this competition can give us an optimistic view on gait recognition.
Gait is one of the most promising biometrics to identify individuals at a long distance. Although most previous methods have focused on recognizing the silhouettes, several end-to-end methods that extract gait features directly from RGB images perform better. However, we demonstrate that these end-to-end methods may inevitably suffer from the gait-irrelevant noises, i.e. low-level texture and color information. Experimentally, we design the cross-domain evaluation to support this view. In this work, we propose a novel end-to-end framework named GaitEdge which can effectively block gait-irrelevant information and release end-to-end training potential. Specifically, GaitEdge synthesizes the output of the pedestrian segmentation network and then feeds it to the subsequent recognition network, where the synthetic silhouettes consist of trainable edges of bodies and fixed interiors to limit the information that the recognition network receives. Besides, GaitAlign for aligning silhouettes is embedded into the GaitEdge without losing differentiability. Experimental results on CASIA-B and our newly built TTG-200 indicate that GaitEdge significantly outperforms the previous methods and provides a more practical end-to-end paradigm. All the source code are available at https://github.com/ShiqiYu/OpenGait.
Gait recognition plays a special role in visual surveillance due to its unique advantage,
e.g.
, long-distance, cross-view and non-cooperative recognition. However, it has not yet been widely applied. One reason for this awkwardness is the lack of a truly big dataset captured in practical outdoor scenarios. Here, the “big” at least means: (1) huge amount of gait videos; (2) sufficient subjects; (3) rich attributes; and (4) spatial and temporal variations. Moreover, most existing large-scale gait datasets are collected indoors, which have few challenges from real scenes, such as the dynamic and complex background clutters, illumination variations, vertical view variations,
etc
. In this article, we introduce a newly built big outdoor gait dataset, called CASIA-E. It contains more than one thousand people distributed over near one million videos. Each person involves 26 view angles and varied appearances caused by changes of bag carrying, dressing and walking styles. The videos are captured across five months and across three kinds of outdoor scenes. Soft biometric features are also recorded for all subjects including age, gender, height, weight, and nationality. Besides, we report an experimental benchmark and examine some meaningful problems that have not been well studied previously,
e.g.
, the influence of million-level training videos, vertical view angles, walking styles, and the thermal infrared modality. We believe that such a big outdoor dataset and the experimental benchmark will promote the development of gait recognition in both academic research and industrial applications.
Gait is a unique biometric feature that can be recognized at a distance; thus, it has broad applications in crime prevention, forensic identification, and social security. To portray a gait, existing gait recognition methods utilize either a gait template which makes it difficult to preserve temporal information, or a gait sequence that maintains unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper, we present a novel perspective that utilizes gait as a
deep set
, which means that a set of gait frames are integrated by a global-local fused deep network inspired by the way our left- and right-hemisphere processes information to learn information that can be used in identification. Based on this
deep set
perspective, our method is
immune to frame permutations
, and can naturally
integrate frames from different videos
that have been acquired under different scenarios, such as diverse viewing angles, different clothes, or different item-carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 96.1 percent on the CASIA-B gait dataset and an accuracy of 87.9 percent on the OU-MVLP gait dataset. Under various complex scenarios, our model also exhibits a high level of robustness. It achieves accuracies of 90.8 and 70.3 percent on CASIA-B under bag-carrying and coat-wearing walking conditions respectively, significantly outperforming the best existing methods. Moreover, the proposed method maintains a satisfactory accuracy even when only small numbers of frames are available in the test samples; for example, it achieves 85.0 percent on CASIA-B even when using only 7 frames. The source code has been released at
https://github.com/AbnerHqC/GaitSet
.
Query expansion is a technique widely used in image search consisting in combining highly ranked images from an original query into an expanded query that is then reissued, generally leading to increased recall and precision. An important aspect of query expansion is choosing an appropriate way to combine the images into a new query. Interestingly, despite the undeniable empirical success of query expansion, ad-hoc methods with different caveats have dominated the landscape, and not a lot of research has been done on learning how to do query expansion. In this paper we propose a more principled framework to query expansion, where one trains, in a discriminative manner, a model that learns how images should be aggregated to form the expanded query. Within this framework, we propose a model that leverages a self-attention mechanism to effectively learn how to transfer information between the different images before aggregating them. Our approach obtains higher accuracy than existing approaches on standard benchmarks. More importantly, our approach is the only one that consistently shows high accuracy under different regimes, overcoming caveats of existing methods.
Gait recognition has gained increasing interest from re- searchers, but there is still no standard evaluation method to compare the performance of different gait recognition algorithms. In this paper, a framework is proposed in an attempt to tackle this problem. The framework consists of a large gait database, a large set of well designed experi- ments and some evaluation metrics. There are 124 subjects in the database, and the gait data was captured from 11 views. Three variations, namely view angle, clothing and carrying condition changes, are separately considered in the database. The database is one of the largest database among the existing databases. Three sets of experiments, including a total of 363 experiments, are designed in the framework. Some metrics are proposed to evaluate gait recognition algorithms.
Exploring deep models for practical gait recognition
- C Fan
- S Hou
- Y Huang
- S Yu
C. Fan, S. Hou, Y. Huang, and S. Yu. Exploring deep
models for practical gait recognition.
arXiv preprint
arXiv:2303.03301, 2023. 4, 5
Gaitmask: Mask-based model for gait recognition
- B Lin
- X Yu
- S Zhang
B. Lin, X. Yu, and S. Zhang. Gaitmask: Mask-based model
for gait recognition. In BMVC, pages 1-12, 2021. 4, 5
Gait recognition in the wild: A benchmark
Z. Zhu, X. Guo, T. Yang, J. Huang, J. Deng, G. Huang,
D. Du, J. Lu, and J. Zhou. Gait recognition in the wild: A
benchmark. In ICCV, 2021. 1, 2, 4
Exploring deep models for practical gait recognition
- Fan
Gaitmask: Mask-based model for gait recognition
- Lin