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6 articles for “PCA based dimensionality reduction”
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Audio-Only Speaker Identification using Principal Component Analysis based Back-Propagation Learning Neural Network in Noisy Environment
Abstract: This paper introduces text dependent speaker identification system on Principal Component Analysis based Back-Propagation learning neural network which deals with detecting a particular speaker from a known populations under noisy environment. For audio pre-processing, ends point detection, silence parts removal, frame segmentation and windowing techniques have been used and wiener filter has been applied to remove the background noise from the audio speech utterances. To reduce the dimension of the …
Published in Current Trends in Signal Processing · Vol. 3, Issue 3, 2013 · pp. 1–10 Read article
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Retina Recognition System using PCA-based Discrete Hidden Markov Model
Abstract: In this paper, a unique approach of retina recognition system has been proposed. Principal component analysis based dimensionality reduction technique has been used here and discrete hidden Markov model (DHMM) has been applied to classify the retinal images. The retinal images are taken from DRIVE retina database. Then some image pre-processing techniques such as image scaling, median filtering-based noise reduction and edge-detection techniques have been used to ready the retinal …
Published in Journal Of Network security · Vol. 3, Issue 1, 2015 · pp. 28–32 Read article
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Performance Evaluation of PCA Based Back Propagation over PCA Based Euclidian Distance for Video Images
Abstract: Key frame selection aims at reducing amount of data and retrieve information desired from a video. Video summarization aims at reducing the amount of data in order to retrieve information from a video. In this paper, we present an innovative approach for key frame selection; and a face detection and recognition from video sequence. For face detection from video, first we select the key frames and then detect multiple faces …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 3, Issue 1, 2016 · pp. 24–31 Read article
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Improved Face Recognition Rate Based on PCA and LDA Using Image Preprocessing Techniques
Abstract: Principal component analysis (PCA) and linear discriminant analysis (LDA) are two powerful tools utilized for dimensionality reduction and feature extraction in most of pattern recognition applications. PCA selects features important for class representation while LDA algorithm selects features that are most effective for class separability. This paper carries out a comparative study of the two important algorithms in the context of enhancement obtained using certain image preprocessing techniques. The implementation …
Published in Journal of Instrumentation Technology & Innovations · Vol. 5, Issue 2, 2015 · pp. 19–24 Read article
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Multi-Scale Analysis of Polymer Based Energy Storage Systems for High Performance Battery Applications
Abstract: The energy storage systems based on polymers are becoming promising materials for the next generation of high performance batteries because of their excellent mechanical flexibility, improved safety, and favorable electrochemical properties. Even with computational tools in Python, polymer-based energy storage systems remain plagued by poor ionic conductivity, complicated electrochemical reactions and potential thermal runaway. Therefore, a multi-scale model is proposed to improve battery performance, thermal stability, reliability, and large-scale deployment …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1035–1048 Read article
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Dimensionality Reduction using Eigenfaces and Fisherfaces for Face Recognition Applications using Kernel LMS Algorithm
Abstract: AbstractFace recognition is an intricate numerical technology in computer systems that can support recognition of human faces using methods such as principal component analysis (PCA), linear discriminant analysis (LDA), etc. by comparing the given facial uniqueness with the already available face database. PCA and LDA are classical feature extraction and data representation techniques used in face recognition algorithm involving unsupervised statistical methods. The 2-D facial image can be transformed into …
Published in Journal of Computer Technology & Applications · Vol. 6, Issue 2, 2015 · pp. 29–55 Read article