scholarly journals A Robust Novel Method for Face Recognition from 2D Depth Images Using DWT and DCT Fusion

2015 ◽  
Vol 46 ◽  
pp. 1518-1528 ◽  
Author(s):  
S. Naveen ◽  
R.S. Moni
Keyword(s):  
Face Recognition ◽  
Depth Images ◽  
Novel Method

scholarly journals Kernel Coupled Cross-Regression for Low-Resolution Face Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Zhifei Wang ◽  
Zhenjiang Miao ◽  
Yanli Wan ◽  
Zhen Tang
Keyword(s):  
Face Recognition ◽  
Time Complexity ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Feature Space ◽  
Low Resolution ◽  
Face Images ◽  
Novel Method ◽  
Nonlinear Embedding ◽  
Spectral Regression

Low resolution (LR) in face recognition (FR) surveillance applications will cause the problem of dimensional mismatch between LR image and its high-resolution (HR) template. In this paper, a novel method called kernel coupled cross-regression (KCCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, KCCR projects LR and HR face images into a unified nonlinear embedding feature space using kernel coupled mappings and graph embedding. Spectral regression is further employed to improve the generalization performance and reduce the time complexity. Meanwhile, cross-regression is developed to fully utilize the HR embedding to increase the information of the LR space, thus to improve the recognition performance. Experiments on the FERET and CMU PIE face database show that KCCR outperforms the existing structure-based methods in terms of recognition rate as well as time complexity.

A novel method for face recognition

2014 ◽  
Author(s):  
Sonhao Zhu ◽  
Xuewei Hu ◽  
Wei Sun ◽  
Ronglin Hu
Keyword(s):  
Face Recognition ◽  
Novel Method

scholarly journals Locally Linear Discriminate Embedding for Face Recognition

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Eimad E. Abusham ◽  
E. K. Wong
Keyword(s):  
Face Recognition ◽  
Dimensional Space ◽  
Linear Structure ◽  
Data Representation ◽  
High Dimensional ◽  
Fast Processing ◽  
Novel Method ◽  
Computational Simplicity ◽  
Low Dimensional ◽  
Locally Linear

A novel method based on the local nonlinear mapping is presented in this research. The method is called Locally Linear Discriminate Embedding (LLDE). LLDE preserves a local linear structure of a high-dimensional space and obtains a compact data representation as accurately as possible in embedding space (low dimensional) before recognition. For computational simplicity and fast processing, Radial Basis Function (RBF) classifier is integrated with the LLDE. RBF classifier is carried out onto low-dimensional embedding with reference to the variance of the data. To validate the proposed method, CMU-PIE database has been used and experiments conducted in this research revealed the efficiency of the proposed methods in face recognition, as compared to the linear and non-linear approaches.

scholarly journals Score Fusion of 3DLBP and ACDN+P for 3D Face Recognition

2016 ◽  
Vol 2 (1) ◽  
pp. 34
Author(s):  
João Baptista Cardia ◽  
Aparecido Nilceu Marana
Keyword(s):  
Face Recognition ◽  
Everyday Life ◽  
Point Cloud ◽  
The Other ◽  
Iterative Closest Point ◽  
3D Face Recognition ◽  
Depth Images ◽  
The Face ◽  
Score Fusion

Many situations of our everyday life require our identification. Biometrics-based methods, besides allowing such identification, can help to prevent frauds. Among several biometrics features, face is one of the most popular due to its intrinsic and important properties, such as universality, acceptability, lowcosts, and covert identification. On the other hand, the traditional automatic face recognition methods based on 2D features can not properly deal with some very frequent challenges, such as occlusion, illumination and pose variations. In this paper we propose a new method for face recognition based on the fusion of 3D low-level local features, ACDN+P and 3DLBP, using depth images captured by cheap Kinect V1 sensors. In order to improve the low quality of the point cloud provided by such devices, Symmetric Filling, Iterative Closest Point, and Savitzky-Golay Filter are used in the preprocessing stage of the proposed method. Experimental results obtained on EURECOM Kinect dataset showed that the proposed method can improve the face recognition rates.

scholarly journals Multiview Layer Fusion Model for Action Recognition Using RGBD Images

2018 ◽  
Vol 2018 ◽  
pp. 1-22
Author(s):  
Pongsagorn Chalearnnetkul ◽  
Nikom Suvonvorn
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Low Complexity ◽  
Feature Model ◽  
Surveillance Systems ◽  
Fusion Model ◽  
Average Precision ◽  
Healthcare Applications ◽  
Depth Images ◽  
Novel Method

Vision-based action recognition encounters different challenges in practice, including recognition of the subject from any viewpoint, processing of data in real time, and offering privacy in a real-world setting. Even recognizing profile-based human actions, a subset of vision-based action recognition, is a considerable challenge in computer vision which forms the basis for an understanding of complex actions, activities, and behaviors, especially in healthcare applications and video surveillance systems. Accordingly, we introduce a novel method to construct a layer feature model for a profile-based solution that allows the fusion of features for multiview depth images. This model enables recognition from several viewpoints with low complexity at a real-time running speed of 63 fps for four profile-based actions: standing/walking, sitting, stooping, and lying. The experiment using the Northwestern-UCLA 3D dataset resulted in an average precision of 86.40%. With the i3DPost dataset, the experiment achieved an average precision of 93.00%. With the PSU multiview profile-based action dataset, a new dataset for multiple viewpoints which provides profile-based action RGBD images built by our group, we achieved an average precision of 99.31%.

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