Forthcoming and Online First Articles

International Journal of Applied Pattern Recognition

International Journal of Applied Pattern Recognition (IJAPR)

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International Journal of Applied Pattern Recognition (2 papers in press)

Regular Issues

  • An effectual multiscale feature extraction in integer wavelet transform domain for illumination invariable face recognition   Order a copy of this article
    by Juhi Chaudhary, Jyotsna Yadav 
    Abstract: Face recognition biometric recognises human faces effectively where their performance is critically affected under deviating light effects. This work presents an efficient illumination invariant feature extraction technique using homomorphic filtering in integer wavelet transform (IWT) domain. The goal of this investigation is to subdue the low frequency components in small-scale extracted features with the simultaneous perpetuation of rugged texture components in face images. The technique exploits homomorphic filtering based illumination normalised (HFIN) images which are then utilised in analysing the low and high pass frequency coefficients. Furthermore, IWT-based multiscale features (MFIWT) over HFIN images are examined with orthogonal and biorthogonal wavelets. The HFIN-MFIWT features are hereafter mapped onto non-correlated lower dimensional subspace using eigenface mechanism. Significant facial features are then classified using K-nearest neighbour. The efficacy of HFIN-MFIWT approach is assessed on Yale, Yale B, CMU-PIE, and extended Yale B databases that evidently authenticate its effectiveness.
    Keywords: integer wavelet transform; IWT; illumination invariant; face recognition; homomorphic filtering; wavelet families.
    DOI: 10.1504/IJAPR.2023.10055129
  • LSTM based Electroencephalography Analysis for Sleep Disorder Subjects   Order a copy of this article
    by SUMEDHA BORDE, Varsha R. Ratnaparkhe 
    Abstract: Electroencephalogram (EEG) is a complex, nonlinear signal which requires extensive training for detection of changes due to sleep disorder. Most of the traditional machine learning algorithms has been used in the past for detection of sleep disorder subjects. Recently deep learning has demonstrated a very promising approach for sensing EEG signals as it has excellent capacity of extracting features from raw signals. The proposed work aims to differentiate sleep disorder subjects from normal subjects using a deep learning-based model. To examine this, open-source EEG dataset from ten different electrodes of six sleep disorder subjects and six normal subjects is used here. Long short-term memory (LSTM) model, a class of recurrent neural network (RNN) is proposed for detection of sleep disorder subjects. Finally, in Table 3, accuracies are compared which are obtained in various models applied on same dataset. It is clearly predicted that the offered LSTM based technique gives classification performance of 70.75% accuracy as compared to other techniques in literature survey. Along with accuracy, recall of 88.34%, precision of 65.35% and specificity of 53.17% is evaluated for proposed LSTM model.
    Keywords: electroencephalogram; EEG; deep learning; long short term memory; LSTM; recurrent neural network; RNN; recall.
    DOI: 10.1504/IJAPR.2023.10060614