scholarly journals Dimensionality reduction methods and image-to-class distance for face recognition and human gait recognition

2010 ◽  
Author(s):  
Yi Huang
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Gait Recognition ◽  
Human Gait ◽  
Reduction Methods

Application of MEEMD in post‐processing of dimensionality reduction methods for face recognition

2018 ◽  
Vol 8 (1) ◽  
pp. 59-68 ◽  
Author(s):  
Abdelghafour Abbad ◽  
Omar Elharrouss ◽  
Khalid Abbad ◽  
Hamid Tairi
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Post Processing ◽  
Reduction Methods

Speech Emotion Recognition Based on Sparse Representation

2013 ◽  
Vol 38 (4) ◽  
pp. 465-470 ◽  
Author(s):  
Jingjie Yan ◽  
Xiaolan Wang ◽  
Weiyi Gu ◽  
LiLi Ma
Keyword(s):  
Dimensionality Reduction ◽  
Emotion Recognition ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Speech Emotion Recognition ◽  
Least Squares Regression ◽  
Computer Science Pedagogy ◽  
Reduction Methods ◽  
Analysis Computer

Abstract Speech emotion recognition is deemed to be a meaningful and intractable issue among a number of do- mains comprising sentiment analysis, computer science, pedagogy, and so on. In this study, we investigate speech emotion recognition based on sparse partial least squares regression (SPLSR) approach in depth. We make use of the sparse partial least squares regression method to implement the feature selection and dimensionality reduction on the whole acquired speech emotion features. By the means of exploiting the SPLSR method, the component parts of those redundant and meaningless speech emotion features are lessened to zero while those serviceable and informative speech emotion features are maintained and selected to the following classification step. A number of tests on Berlin database reveal that the recogni- tion rate of the SPLSR method can reach up to 79.23% and is superior to other compared dimensionality reduction methods.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

scholarly journals Supervised dimensionality reduction for big data

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Joshua T. Vogelstein ◽  
Eric W. Bridgeford ◽  
Minh Tang ◽  
Da Zheng ◽  
Christopher Douville ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Data Science ◽  
Real Data ◽  
Low Rank ◽  
Conditional Moment ◽  
Desktop Computer ◽  
Reduction Techniques ◽  
Reduction Methods ◽  
The Individual ◽  
Low Dimensional

AbstractTo solve key biomedical problems, experimentalists now routinely measure millions or billions of features (dimensions) per sample, with the hope that data science techniques will be able to build accurate data-driven inferences. Because sample sizes are typically orders of magnitude smaller than the dimensionality of these data, valid inferences require finding a low-dimensional representation that preserves the discriminating information (e.g., whether the individual suffers from a particular disease). There is a lack of interpretable supervised dimensionality reduction methods that scale to millions of dimensions with strong statistical theoretical guarantees. We introduce an approach to extending principal components analysis by incorporating class-conditional moment estimates into the low-dimensional projection. The simplest version, Linear Optimal Low-rank projection, incorporates the class-conditional means. We prove, and substantiate with both synthetic and real data benchmarks, that Linear Optimal Low-Rank Projection and its generalizations lead to improved data representations for subsequent classification, while maintaining computational efficiency and scalability. Using multiple brain imaging datasets consisting of more than 150 million features, and several genomics datasets with more than 500,000 features, Linear Optimal Low-Rank Projection outperforms other scalable linear dimensionality reduction techniques in terms of accuracy, while only requiring a few minutes on a standard desktop computer.

Video-based Indoor Human Gait Recognition Using Depth Imaging and Hidden Markov Model: A Smart System for Smart Home

2010 ◽  
Vol 20 (1) ◽  
pp. 120-128 ◽  
Author(s):  
Md. Zia Uddin ◽  
Tae-Seong Kim ◽  
Jeong Tai Kim
Keyword(s):  
Hidden Markov ◽  
Gait Recognition ◽  
Smart Homes ◽  
The Elderly ◽  
Recognition System ◽  
Home Healthcare ◽  
Human Gait ◽  
Depth Imaging ◽  
Smart System ◽  
Spatiotemporal Features

Smart homes that are capable of home healthcare and e-Health services are receiving much attention due to their potential for better care of the elderly and disabled in an indoor environment. Recently the Center for Sustainable Healthy Buildings at Kyung Hee University has developed a novel indoor human activity recognition methodology based on depth imaging of a user’s activities. This system utilizes Independent Component Analysis to extract spatiotemporal features from a series of depth silhouettes of various activities. To recognise the activities from the spatiotemporal features, trained Hidden Markov Models of the activities would be used. In this study, this technique has been extended to recognise human gaits (including normal and abnormal). Since this system could be of great significance for the caring of the elderly, to promote and preserve their health and independence, the gait recognition system would be considered a primary function of the smart system for smart homes. The indoor gait recognition system is trained to detect abnormal gait patterns and generate warnings. The system works in real-time and is aimed to be installed at smart homes. This paper provides the information for further development of the system for their application in the future.

Automatic configuration of spectral dimensionality reduction methods

2010 ◽  
Vol 31 (12) ◽  
pp. 1720-1727 ◽  
Author(s):  
Michał Lewandowski ◽  
Dimitrios Makris ◽  
Jean-Christophe Nebel
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Methods ◽  
Spectral Dimensionality
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