Pose Variation Adaptation for Person Re- identification

2021 ◽  
Author(s):  
Lei Zhang ◽  
Na Jiang ◽  
Yue Xu ◽  
Qishuai Diao ◽  
Zhong Zhou ◽  
...  
Keyword(s):  
Pose Variation

scholarly journals AN EFFECTIVE COLOR FACE RECOGNITION BASED ON BEST COLOR FEATURE SELECTION ALGORITHM USING WEIGHTED FEATURES FUSION SYSTEM

2013 ◽  
Vol 8 (2) ◽  
pp. 787-795
Author(s):  
Sasi Kumar Balasundaram ◽  
J. Umadevi ◽  
B. Sankara Gomathi
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Feature Fusion ◽  
Recognition Performance ◽  
Feature Selection Method ◽  
Recognition Method ◽  
Color Feature ◽  
Color Component ◽  
Pose Variation ◽  
Color Face Recognition

This paper aims to achieve the best color face recognition performance. The newly introduced feature selection method takes advantage of novel learning which is used to find the optimal set of color-component features for the purpose of achieving the best face recognition result. The proposed color face recognition method consists of two parts namely color-component feature selection with boosting and color face recognition solution using selected color component features. This method is better than existing color face recognition methods with illumination, pose variation and low resolution face images. This system is based on the selection of the best color component features from various color models using the novel boosting learning framework. These selected color component features are then combined into a single concatenated color feature using weighted feature fusion. The effectiveness of color face recognition method has been successfully evaluated by the public face databases.

scholarly journals Analysis and Best Parameters Selection for Person Recognition based on Gait model using CNN algorithm and Image Augmentation

2020 ◽  
Author(s):  
Abeer Saleh ◽  
Talal Hamoud
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Person Recognition ◽  
Critical Issues ◽  
Dataset Size ◽  
Deep Convolution Neural Network ◽  
Human Pose ◽  
Improved Model ◽  
Pose Variation ◽  
Best Parameters

Abstract Person recognition based on gait model and motion print is indeed a challenging and novel task due to its usages and to the critical issues of human pose variation, human body occlusion, camera view variation, etc. In this project, a deep convolution neural network (CNN) was modified and adapted for person recognition with image augmentation technique. CNN is best algorithm of deep learning algorithms. Adaptation aims to get best values for CNN parameters to get best CNN model. In Addition to the CNN parameters, the design of CNN model itself was adapted to get best model design; number of layers and normalization between them. After choosing best parameters and best design, Image augmentation was used to increase train dataset with many copies of the image to boost the number of different images that will be used to train Deep learning algorithms. The tests were achieved using known dataset (Market dataset). The dataset contains sequential pictures of people in different gait status. The image in CNN model as matrix is extracted to many images or matrices, so dataset size may be bigger by hundred times to make the problem a big data problem, in this project Results show that adaptation has improved the accuracy of person recognition using gait model, that is represented in many successive images for the same person. In addition, dataset contains images of person carrying things. The improved model of CNN is robust to image dimensions (quality and resolution) and to carried things by persons.

Performance Analysis of Pose Invariant Face Recognition Approaches in Unconstrained Environments

2015 ◽  
Vol 5 (1) ◽  
pp. 66-81 ◽  
Author(s):  
M. Parisa Beham ◽  
S. M. Mansoor Roomi ◽  
J. Alageshan ◽  
V. Kapileshwaran
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Algorithm ◽  
K Nearest Neighbor ◽  
Research Issues ◽  
Histograms Of Oriented Gradients ◽  
Recognition Phase ◽  
The Face ◽  
Pose Variation

Face recognition and authentication are two significant and dynamic research issues in computer vision applications. There are many factors that should be accounted for face recognition; among them pose variation is a major challenge which severely influence in the performance of face recognition. In order to improve the performance, several research methods have been developed to perform the face recognition process with pose invariant conditions in constrained and unconstrained environments. In this paper, the authors analyzed the performance of a popular texture descriptors viz., Local Binary Pattern, Local Derivative Pattern and Histograms of Oriented Gradients for pose invariant problem. State of the art preprocessing techniques such as Discrete Cosine Transform, Difference of Gaussian, Multi Scale Retinex and Gradient face have also been applied before feature extraction. In the recognition phase K- nearest neighbor classifier is used to accomplish the classification task. To evaluate the efficiency of pose invariant face recognition algorithm three publicly available databases viz. UMIST, ORL and LFW datasets have been used. The above said databases have very wide pose variations and it is proved that the state of the art method is efficient only in constrained situations.

scholarly journals An improved boundary-aware face alignment using stacked dense U-Nets

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094090
Author(s):  
Jianghao Ye ◽  
Ying Cui ◽  
Xiang Pan ◽  
Herong Zheng ◽  
Dongyan Guo ◽  
...  
Keyword(s):  
Message Passing ◽  
Face Alignment ◽  
Shape Variation ◽  
Facial Landmark Localization ◽  
Facial Landmark ◽  
Public Datasets ◽  
Pose Variation ◽  
Two Stages ◽  
Landmark Localization ◽  
Boundary Lines

Facial landmark localization is still a challenge task in the unconstrained environment with influences of significant variation conditions such as facial pose, shape, expression, illumination, and occlusions. In this work, we present an improved boundary-aware face alignment method by using stacked dense U-Nets. The proposed method consists of two stages: a boundary heatmap estimation stage to learn the facial boundary lines and a facial landmark localization stage to predict the final face alignment result. With the constraint of boundary lines, facial landmarks are unified as a whole facial shape. Hence, the unseen landmarks in a shape with occlusions can be better estimated by message passing with other landmarks. By introducing the stacked dense U-Nets for feature extraction, the capacity of the model is improved. Experiments and comparisons on public datasets show that the proposed method obtains better performance than the baselines, especially for facial images with large pose variation, shape variation, and occlusions.

scholarly journals STA: Spatial-Temporal Attention for Large-Scale Video-Based Person Re-Identification

2019 ◽  
Vol 33 ◽  
pp. 8287-8294 ◽  
Author(s):  
Yang Fu ◽  
Xiaoyang Wang ◽  
Yunchao Wei ◽  
Thomas Huang
Keyword(s):  
Large Scale ◽  
Feature Representation ◽  
Temporal Attention ◽  
Video Clips ◽  
The Arts ◽  
Temporal Dimensions ◽  
Pose Variation ◽  
Inter Frame ◽  
Score Matrix ◽  
Discriminative Parts

In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person reidentification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the proposed STA adopts a more effective way for producing robust clip-level feature representation. Concretely, our STA fully exploits those discriminative parts of one target person in both spatial and temporal dimensions, which results in a 2-D attention score matrix via inter-frame regularization to measure the importances of spatial parts across different frames. Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix. In this way, the challenging cases for video-based person re-identification such as pose variation and partial occlusion can be well tackled by the STA. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMCVideoReID. In particular, the mAP reaches 87.7% on MARS, which significantly outperforms the state-of-the-arts with a large margin of more than 11.6%.

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