KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Titijaroonroj, TaravichetSubject: search.filters.subject.PCA

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Heart Rate Estimation by PCA with LSTM from Video-based Plethysmography Under Periodic Noise
    (2022-01-01)
    Traivinidsreesuk, Chetsadaporn
    ;
    Yodrabum, Nutcha
    ;
    Chaikangwan, Irin
    ;
    Titijaroonroj, Taravichet
    A remote photoplethysmography (rPPG) analysis can extract vital signs from the source video, including heart rate estimation. One of the problems of heart rate estimation is periodic noise embedded in the source video. It is difficult for an rPPG analysis to discriminate between vital signal information and noise, increasing prediction error. To alleviate this problem, this paper used principal component analysis (PCA) to extract rPPG signals from the input video before forwarding the signal to Long Short Term Memory (LSTM) to estimate heart rate. The experimental results show that, among discrete Fourier Transform method, neural networks, and neural network with LSTM, the proposed method accomplished a much lower MAEP at 15.05, 13.90, and 17.90 in the cases of overall, with no periodic noise, and with periodic noise, respectively.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    An improved 2DPCA for face recognition under illumination effects
    (2015-01-01)
    Woraratpanya, Kuntpong
    ;
    Sornnoi, Monmorakot
    ;
    Leelaburanapong, Savita
    ;
    Titijaroonroj, Taravichet
    ;
    Varakulsiripunth, Ruttikorn
    Principal component analysis (PCA) is one of the successful techniques for applying to face recognition, but its challenge still remains for solving an illumination effect condition. This paper proposes an improved 2DPCA (I-2DPCA) for overwhelming the illumination effect in face recognition. The proposed method is based on two assumptions. The first assumption is to create the covariance matrix that can effectively decompose the components of illumination effects from the eigenfaces. This avoids the illumination effect problem. The second assumption is to select the suitable eigenvectors that can significantly improve the recognition rate. Based on the Extended Yale Face Database B+ containing 60 illumination conditions, the experimental results show that not only does the proposed method decrease the computing time, but it also improves the recognition rate up to 95.93%.