Rank-1 Accuracy Research Articles

Efficient similarity search on multidimensional space of biometric databases

The problem of pursuing the data items of a large database whose distances to a query item are the least is known as Similarity Search (Nearest Neighbor Search) problem. There exist various algorithms to address this problem. Some of the well known algorithms are i) exact algorithms ii) approximation algorithms and iii) randomized algorithms. This paper has made study only on exact and approximation algorithms because randomized algorithm produces approximate results with some probability. Recently, there are several approximation algorithms are proposed by the researchers because this type of algorithms minimizes the problem of Curse of Dimensionality.This paper mainly has two major sections. In first section, various methods under exact and approximation algorithms are discussed with regard to storage, preprocessing and query time. In the second section, efficient algorithms for similarity search suitable for certain physiological characteristics based biometric systems are considered. Biometric system has five main steps viz acquisition of Image, pre-processing, extraction of features, matching and making final decision. In this paper, indexing algorithms for similarity search suitable for iris trait based on different features are discussed in detail. Since the nature of features are distinct and different in biometric traits, there does not exist a universal (one unique) solution which can apply to all traits of biometric systems. Various performance measures like Penetration Rate and Hit Rate are used to determine the correct recognition rate with top best match (rank-1 accuracy).

StyleGAN-LSRO Method for Person Re-Identification

In this study, the StyleGAN-LSRO method has been developed for person re-identification (re-ID) tasks. This method applies the style-based generative adversarial network (StyleGAN) to generate new synthetic images from existing person re-ID datasets and the label smoothing regularization for outliers (LSRO) algorithm to process those newly produced unlabeled images by assigning them a uniform label distribution along with the definition of a loss function for the training process. A baseline model based on a convolutional neural network (CNN) was developed to learn the discriminative features to recognize a person's identity. The developed method has been tested on three datasets. These datasets are Market-1501, DukeMTMC-reID, and MSMT17. The experimental results show that the StyleGAN model achieved a Fréchet inception distance score of 12.67 and structural similarity score of 0.387, outperforming all the previous generative methods and demonstrating that the images generated by StyleGAN are of superior quality. Adding these StyleGAN-generated data significantly improves the person re-ID accuracy. The StyleGAN-LSRO person re-ID method achieved 98.5% rank-1 accuracy and 91.8% mean average precision (mAP) on Market-1501, 87.0% rank-1 accuracy and 83.8% mAP on DukeMTMC-reID, and 81.5% rank-1 accuracy and 60.9% mAP on MSMT17, respectively. These results show that the StyleGAN-LSRO method significantly outperforms most of the state-of-the-art person re-ID methods. The success rate for person re-ID increases when the images used are of high resolution and square matrix form. In other cases, the success rate decreases.

Incremental Generative Occlusion Adversarial Suppression Network for Person ReID.

Person re-identification (re-id) suffers from the significant challenge of occlusion, where an image contains occlusions and less discriminative pedestrian information. However, certain work consistently attempts to design complex modules to capture implicit information (including human pose landmarks, mask maps, and spatial information). The network, consequently, focuses on discriminative features learning on human non-occluded body regions and realizes effective matching under spatial misalignment. Few studies have focused on data augmentation, given that existing single-based data augmentation methods bring limited performance improvement. To address the occlusion problem, we propose a novel Incremental Generative Occlusion Adversarial Suppression (IGOAS) network. It consists of 1) an incremental generative occlusion block, generating easy-to-hard occlusion data, that makes the network more robust to occlusion by gradually learning harder occlusion instead of hardest occlusion directly. And 2) a global-adversarial suppression (G&A) framework with a global branch and an adversarial suppression branch. The global branch extracts steady global features of the images. The adversarial suppression branch, embedded with two occlusion suppression module, minimizes the generated occlusion's response and strengthens attentive feature representation on human non-occluded body regions. Finally, we get a more discriminative pedestrian feature descriptor by concatenating two branches' features, which is robust to the occlusion problem. The experiments on the occluded dataset show the competitive performance of IGOAS. On Occluded-DukeMTMC, it achieves 60.1% Rank-1 accuracy and 49.4% mAP.

A Two-Stream Dynamic Pyramid Representation Model for Video-Based Person Re-Identification.

Video-based person re-identification (Re-ID) leverages rich spatio-temporal information embedded in sequence data to further improve the retrieval accuracy comparing with single image Re-ID. However, it also brings new difficulties. 1) Both spatial and temporal information should be considered simultaneously. 2) Pedestrian video data often contains redundant information and 3) suffers from data quality problems such as occlusion, background clutter. To solve the above problems, we propose a novel two-stream Dynamic Pyramid Representation Model (DPRM). DPRM mainly consists of three sub-models, i.e., Pyramidal Distribution Sampling Method (PDSM), Dynamic Pyramid Dilated Convolution (DPDC) and Pyramid Attention Pooling (PAP). PDSM is applied for more effective data pre-processing according to sequence semantic distribution. DPDC and PAP can be considered as two streams to describe the motion context and static appearance of a video sequence, respectively. By fusing the two-stream features together, we finally achieve comprehensive spatio-temporal representation. Notably, dynamic pyramid strategy is applied throughout the whole model. This strategy exploits multi-scale features under attention mechanism to maximally capture the most discriminative features and mitigate the impact of video data quality problems such as partial occlusion. Extensive experiments demonstrate the outperformance of DPRM. For instance, it achieves 83.0% mAP and 89.0% Rank-1 accuracy on MARS dataset and reaches state of the art.

Semantic-Guided Pixel Sampling for Cloth-Changing Person Re-Identification

Cloth-changing person re-identification (re-ID) is a new rising research topic that aims at retrieving pedestrians whose clothes are changed. This task is quite challenging and has not been fully studied to date. Current works mainly focus on body shape or contour sketch, but they are not robust enough due to view and posture variations. The key to this task is to exploit cloth-irrelevant cues. This paper proposes a semantic-guided pixel sampling approach for the cloth-changing person re-ID task. We do not explicitly define which feature to extract but force the model to automatically learn cloth-irrelevant cues. Specifically, we first recognize the pedestrian's upper clothes and pants, then randomly change them by sampling pixels from other pedestrians. The changed samples retain the identity labels but exchange the pixels of clothes or pants among different pedestrians. Besides, we adopt a loss function to constrain the learned features to keep consistent before and after changes. In this way, the model is forced to learn cues that are irrelevant to upper clothes and pants. We conduct extensive experiments on the latest released PRCC dataset. Our method achieved 65.8% on Rank1 accuracy, which outperforms previous methods with a large margin. The code is available at https://github.com/shuxjweb/pixel_sampling.git.

Progressive Feature Enhancement for Person Re-Identification

Most of person Re-Identification (ReID) works extract features from the top CNN layer for person image matching. The top CNN layer commonly corresponds to large receptive fields, thus is not effective in depicting visual cues at multiple scales, e.g. , both global appearance and local details. This work proposes a Progressive Feature Enhancement (PFE) algorithm to spot and fuse multi-scale discriminative cues from different CNN layers into a single feature vector. The basic idea is to progressively learn complementary features with a layer-specific supervision from deep to shallow layers. The layer-specific supervision is inferred by the proposed Masked Feature Augmentation (MFA) module. For each CNN layer, MFA indicates cues that have been captured in its deeper layers. MFA hence supervises each layer to depict additional visual cues missed by its deeper layers. This framework effectively learns multi-scale features without requiring extra part annotations or dividing body parts. To further facilitate the layer-specific feature generation, a Two-Stage Attention Module (TSAM) is proposed to filter pixel-wise and channel-wise noises on intermediate feature maps. Extensive experiments on four ReID datasets show that our approach achieves competitive performance, e.g. , with ResNet50 backbone, it achieves rank1 accuracy of 95.1%, 88.2%, 79.1% and 71.6% on Market-1501 , DukeMTMC-ReID , MSMT17 and CUHK03 Detected , respectively, outperforming many state-of-the-art works.

Random style transfer for person re-identification with one example

Person re-identification with only one labeled image for each identify can not eliminate the style variations of different cameras in the same dataset. In this paper, we propose a random style transfer strategy that randomly transforms the labeled images on the one-example person re-identification task. In this strategy, we focus on twofolds: 1) Randomly transform the camera style of labeled images and unlabeled images during the training stage and 2) use the average feature of labeled data and its camera style transform data to estimate pseudo label on unlabeled data. Notably, our strategy exhibits state-of-the-art performance on large-scale image datasets and its Rank-1 accuracy outperforms the state-of-the-art method by 10.3% points on Market-1501, and 8.4% points on DukeMTMC-reID.

Part-Relation-Aware Feature Fusion Network for Person Re-Identification

The research of part-based methods has been proven as an effective way in person re-identification (Re-ID) task. However, in existing part-based Re-ID methods, the informative interactions and potential associations among parts are neglected, which demands further study. To fill this gap, we propose a novel Part-relation-aware Feature Fusion Network (PFFN) which achieves a part-level feature fusion and enhances the discrimination of part features by fully employing helpful information from associations among parts. More specifically, a Dual-stage Attention (DA) module, consisting of spatial and part-based channel attention, is proposed to exploit complementary benefits of two kinds of attention information, thereby facilitating model with learning more discriminative features. Furthermore, Part-relation Exploitation (PE) module is proposed to learn relation-aware part features where correlative information among parts are fully employed, thereby bringing a noticeable improvement in performance. Extensive experiments are conducted on four mainstream Re-ID datasets to verify the superiority of PFFN. Compared with baseline model, PFFN has gained rank-1 accuracy improvement of 18.2% on MSMT17-v2, 12.1% on the CUHK03-Labeled, 5.6% on DukeMTMC-reid and 1.3% on Market1501, compellingly validating its effectiveness.

Bidirectional Interaction Network for Person Re-Identification.

Person re-identification (ReID) task aims to retrieve the same person across multiple spatially disjoint camera views. Due to huge image changes caused by various factors such as posture variation and illumination transformation, images of different persons may share the more similar appearances than images of the same one. Learning discriminative representations to distinguish details of different persons is significant for person ReID. Many existing methods learn discriminative representations resorting to a human body part location branch which requires cumbersome expert human annotations or complex network designs. In this article, a novel bidirectional interaction network is proposed to explore discriminative representations for person ReID without any human body part detection. The proposed method regards multiple convolutional features as responses to various body part properties and exploits the inter-layer interaction to mine discriminative representations for person identities. Firstly, an inter-layer bilinear pooling strategy is proposed to feasibly exploit the pairwise feature relations between two convolution layers. Secondly, to explore interaction of multiple layers, an effective bidirectional integration strategy consisting of two different multi-layer interaction processes is designed to aggregate bilinear pooling interaction of multiple convolution layers. The interaction of multiple layers is implemented in a layer-by-layer nesting policy to ensure the two interaction processes are different and complementary. Extensive experiments validate the superiority of the proposed method on four popular person ReID datasets including Market-1501, DukeMTMC-ReID, CUHK03-NP and MSMT17. Specifically, the proposed method achieves a rank-1 accuracy of 95.1% and 88.2% on Market-1501 and DukeMTMC-ReID, respectively.

GaitSet: Cross-View Gait Recognition Through Utilizing Gait As a Deep Set.

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.

Bi-Directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification.

RGB-Infrared person re-identification (RGB-IR Re-ID) is a cross-modality matching problem, where the modality discrepancy is a big challenge. Most existing works use Euclidean metric based constraints to resolve the discrepancy between features of images from different modalities. However, these methods are incapable of learning angularly discriminative feature embedding because Euclidean distance cannot measure the included angle between embedding vectors effectively. As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors. Moreover, to help stabilize and learn the magnitudes of embedding vectors, we adopt a common space batch normalization layer. The quantitative and qualitative experiments on the SYSU-MM01 and RegDB dataset support our analysis. On SYSU-MM01 dataset, the performance is improved from 7.40% / 11.46% to 38.57% / 38.61% for rank-1 accuracy / mAP compared with the baseline. The proposed method can be generalized to the task of single-modality Re-ID and improves the rank-1 accuracy / mAP from 92.0% / 81.7% to 94.7% / 86.6% on the Market-1501 dataset, from 82.6% / 70.6% to 87.6% / 77.1% on the DukeMTMC-reID dataset.

Mask‐guided class activation mapping network for person re‐identification

In this Letter, the authors propose a novel mask-guided class activation mapping (MCAM) network for person re-identification, which learns background-invariant and view-invariant features. Specifically, a novel loss function named mask-guided mapping loss is meticulously formulated to utilise the human binary masks, which contain helpful body shape information as the reference standard, thereby guiding the model to place more emphasis on human body regions. Moreover, they propose a new weighted channel attention (WCA) module, which replaces the global average pooling with a global depthwise convolution layer. By virtue of this particular WCA module, the feature information distributed across the spatial space can be individually weighted and dynamically compressed into a more precise channel attention map. Extensive experiments have been carried out on three widely-used re-identification data sets. Compared with the baseline model, MCAM has gained rank-1 accuracy improvement of 2.0% on Market-1501, 6.0% on DukeMTMC-reID, and 7.5% on CUHK03-NP, confirming its effectiveness.

STNReID: Deep Convolutional Networks With Pairwise Spatial Transformer Networks for Partial Person Re-Identification

Partial person re-identification (ReID) is a challenging task because only partial information of person images is available for matching target persons. Few studies, especially on deep learning, have focused on matching partial person images with holistic person images. This study presents a novel deep partial ReID framework based on pairwise spatial transformer networks (STNReID), which can be trained on existing holistic person datasets. STNReID includes a spatial transformer network (STN) module and a ReID module. The STN module samples an affined image (a semantically corresponding patch) from the holistic image to match the partial image. The ReID module extracts the features of the holistic, partial, and affined images. Competition (or confrontation) is observed between the STN module and the ReID module, and two-stage training is applied to acquire a strong STNReID for partial ReID. Experimental results show that our STNReID obtains 66.7% and 54.6% rank-1 accuracies on partial ReID and partial iLIDS datasets, respectively. These values are at par with those obtained with state-of-the-art methods.

Depth occlusion perception feature analysis for person re-identification

• Occlusion perception person re-identification. • Research on deep neural network framework. • Person re-identification dataset GAN generation. Person re-identification (ReID) has achieved significant improvement under the setting of matching two holistic person images. However, persons are easily occluded by the various objects and other persons in real-world scenarios, making Person ReID a challenging task. In this paper, we propose a novel method named Pose-Driven Visibility Model (PDVM) to effectively solve the degradation of recognition performance caused by occlusion. Firstly, we extract non-occluded human body features through pose estimation, pay attention to the salient features of non-human parts through self-attention mechanism, and obtains the final feature representation after the combination. Secondly, we more accurately locate person body parts by utilizing the detected human keypoints in different occlusion situations, effectively reducing the impact of unalignment and realizing better matching for persons. We implement extensive experiments on Occluded-DukeMTMC and Partial-REID. Our proposed method achieves state of the art performances which reaches 53.0% Rank-1 accuracy on Occluded-DukeMTMC dataset and ablation analysis also verify the effectiveness of our method. 2020 Elsevier Ltd. All rights reserved

Multiscale Omnibearing Attention Networks for Person Re-Identification

The past few years in the fields of Person Re-Identification (RE-ID) have seen attention mechanism receives enormous interest as it has superior performance in obtaining discriminative feature representations. However, a wide range of state-of-the-art RE-ID attention models only focus on one-dimensional attention design method, e.g. spatial attention and channels attention, hence the produced attention maps are neither detailed enough nor discriminative enough to capture complicated interactions of visual parts. Developing multi-scale attention mechanism for RE-ID, an under-studied approach, becomes a practicable method to overcome this deficiency. Toward this goal, we propose a Multiscale Omnibearing Attention Networks (MOAN) for RE-ID which is capable of utilizing the complex fusion information acquired from the multiscale attention mechanism with features being more representative. Specifically, MOAN takes full advantage of multi-sized convolution filters to obtain discriminative holistic and local feature maps, and adaptively conducts feature information augmentation by introducing an Omnibearing Attention (OA) module. Through the OA module, spatial attention and channel attention are integrated together in a unique way where they work in a complementary way. To sum up, MOAN not only inherits the merit of two kinds of attention mechanism but also performs well in extracting comprehensive feature information. Furthermore, taking into account the robustness of model performance, we formulate a Random Drop (RD) Function to facilitate training MOAN and further increase the diversity of training model for adaptation. Furthermore, to achieve end-to-end training, we utilize trainable parameters to take place of initial fixed parameters, and the model performance is experimentally promoted. Extensive experiments have been carried out on the four mainstream RE-ID datasets. As the result shows, our method with re-ranking achieves rank-1 accuracy of 92.29% on CUHK03-NP, 97.45% on Market-1501, 93.81% on DukeMTMC-reID and 81.53% on MSMT17-V2, outperforming the state-of-the-art methods and confirming the effectiveness of our method.

Non-Local Spatial and Temporal Attention Network for Video-Based Person Re-Identification

Given a video containing a person, the video-based person re-identification (Re-ID) task aims to identify the same person from videos captured under different cameras. How to embed spatial-temporal information of a video into its feature representation is a crucial challenge. Most existing methods have failed to make full use of the relationship between frames during feature extraction. In this work, we propose a plug-and-play non-local attention module (NLAM) for frame-level feature extraction. NLAM, based on global spatial attention and channel attention, helps the network to determine the location of the person in each frame. Besides, we propose a non-local temporal pooling (NLTP) method used for temporal features’ aggregation, which can effectively capture long-range and global dependencies among the frames of the video. Our model obtained impressive results on different datasets compared to the state-of-the-art methods. In particular, it achieved the rank-1 accuracy of 86.3% on the MARS (Motion Analysis and Re-identification Set) dataset without re-ranking, which is 1.4% higher than the state-of-the-art way. On the DukeMTMC-VideoReID (Duke Multi-Target Multi-Camera Video Reidentification) dataset, our method also had an excellent performance of 95% rank-1 accuracy and 94.5% mAP (mean Average Precision).

Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-Identification

Many unsupervised approaches have been proposed recently for the video-based re-identification problem since annotations of samples across cameras are time-consuming. However, higher-order relationships across the entire camera network are ignored by these methods, leading to contradictory outputs when matching results from different camera pairs are combined. In this paper, we address the problem of unsupervised video-based re-identification by proposing a consistent cross-view matching (CCM) framework, in which global camera network constraints are exploited to guarantee the matched pairs are with consistency. Specifically, we first propose to utilize the first neighbor of each sample to discover relations among samples and find the groups in each camera. Additionally, a cross-view matching strategy followed by global camera network constraints is proposed to explore the matching relationships across the entire camera network. Finally, we learn metric models for camera pairs progressively by alternatively mining consistent cross-view matching pairs and updating metric models using these obtained matches. Rigorous experiments on two widely-used benchmarks for video re-identification demonstrate the superiority of the proposed method over current state-of-the-art unsupervised methods; for example, on the MARS dataset, our method achieves an improvement of 4.2\% over unsupervised methods, and even 2.5\% over one-shot supervision-based methods for rank-1 accuracy.

Appearance feature enhancement for person re-identification

Person re-identification (Re-ID) has important practical application value in intelligent video analysis. Due to the illumination, occlusion, and pose variation, person Re-ID is still a challenging problem. Some recent Re-ID methods based on ResNet-50 have achieved high accuracy, but performance degradation is caused by pose variation. To address this issue, Pose-Invariant Convolutional Baseline (PICB) embed with the proposed Pooling Fusion Block (PFB) is put forward as a new baseline for person Re-ID task. On the basis of PICB, an end-to-end network named Appearance-Enhanced Feature Learning Network (AEFLN) is proposed to simultaneously learn diversity body features and discriminative part features. Specially, a novel (DBFL) strategy is presented to learn diversity body features, which could alleviate the potential local minima problem generated by optimizing model with randomly initialized parameters in PFB. In addition, uniform part-level feature extractors are applied to learn part features, which compensates for body features’ lack of distinguishable local information. In testing phase, body features and part features are integrated to represent the enhanced appearance feature for each person image. Comprehensive experiments have demonstrated that our method can outperform the sate-of-the-art results on several public available datasets, including Market-1501, CUHK03 and DukeMTMC-reID. For instance, we achieve 74.8% (+11.1%) and 76.5% (+19.0%) in Rank-1 accuracy and mAP on CUHK03 dataset.

Model compression via pruning and knowledge distillation for person re-identification

Person re-identification (ReID) is an important problem in intelligent monitoring. Recently, with the development of deep learning, convolutional neural networks have achieved state-of-the-art performance on person ReID problems. However, the deep neural network models used by these methods tend to have large number of parameters and high computational cost, thereby hindering their deployment on resource-constraint devices or real-time applications. In this study, we propose a method that distills the knowledge to a pruned model to reduce the parameters, which can be divided into two stages: one is to apply unstructured pruning method on over-parameterized models, whereas the other is to carry out representation and metric learning-based knowledge distillation on the model after pruning to improve performance. Finally, the proposed method can effectively reduce the total number of parameters by 8.4 with only 0.1% drop of rank-1 accuracy on the Market1501 dataset and no drop of rank-1 accuracy on the DukeMTMC-reID dataset.

Automatic human identification from panoramic dental radiographs using the convolutional neural network

Human identification is an important task in mass disaster and criminal investigations. Although several automatic dental identification systems have been proposed, accurate and fast identification from panoramic dental radiographs (PDRs) remains a challenging issue. In this study, an automatic human identification system (DENT-net) was developed using the customized convolutional neural network (CNN). The DENT-net was trained on 15,369 PDRs from 6300 individuals. The PDRs were preprocessed by affine transformation and histogram equalization. The DENT-net took 128 × 128 × 7 square patches as input, including the whole PDR and six details extracted from the PDR. Using the DENT-net, the feature extraction took around 10 milliseconds per image and the running time for retrieval was 33.03 milliseconds in a 2000-individual database, promising an application on larger databases. The visualization of CNN showed that the teeth, maxilla, and mandible all contributed to human identification. The DENT-net achieved Rank-1 accuracy of 85.16% and Rank-5 accuracy of 97.74% for human identification. The present results demonstrated that human identification can be achieved from PDRs by CNN with high accuracy and speed. The present system can be used without any special equipment or knowledge to generate the candidate images. While the final decision should be made by human specialists in practice. It is expected to aid human identification in mass disaster and criminal investigation.