Title: An effectual multiscale feature extraction in integer wavelet transform domain for illumination invariable face recognition

Authors: Juhi Chaudhary; Jyotsna Yadav

Addresses: University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, 110078, India ' University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, 110078, India

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.2024.146813

International Journal of Applied Pattern Recognition, 2024 Vol.7 No.3/4, pp.161 - 192

Received: 07 Feb 2022
Accepted: 07 Feb 2023
Published online: 19 Jun 2025 *