An effectual multiscale feature extraction in integer wavelet transform domain for illumination invariable face recognition
Juhi Chaudhary, Jyotsna Yadav · International Journal of Applied Pattern Recognition · 2024
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.