English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Face recognition has recently been the centre of attention of many researchers (Jain et al. 2004). The earliest work in digital face recognition was reported by Bledsoe in 1964. Statistical face recognition systems such as principal component analysis (PCA) based eigenfaces introduced by Turk and Pentland in 1991, attracted lots of attention. Fisherfaces method based on linear discriminant analysis was introduced later on by Belhumeur et al. (1997). Many of these methods are based on grey scale images; however colour images are increasingly being used since they add additional biometric information for face recognition (Marcel and Bengio, 381). PDFs obtained from different colour channels of a face image can be considered as the signature of the face, which can be used to represent the face image in a low dimensional space (Demirel and Anbarjafari, VISSAP 2008). Images with small changes in translation, rotation and illumination still possess high correlation in their corresponding PDFs. PDF of an image is a normalized version of an image histogram which have been used in many image processing applications such as object detection (Laptev, 2006) and face recognition (Yoo and Oh, 1999; Rodriguez and Marcel, 2006; Demirel and Anbarjafari, IEEE Signal Processing Letter, 2008). Nowadays, wavelets have been used quite frequently in image processing. It has been used for feature extraction (Wang and chen, 2006), denoising (Starck et al., 2002), compression (Lamard et al. 2005), and face recognition (Liu et al., 2007; Demirel et al., 2008). The decomposition of images into different frequency ranges permits the isolation of the frequency components introduced by “intrinsic deformations” or “extrinsic factors” into certain subbands (Dai and Yan, 2007). This process results in isolating small changes in an image mainly in high frequency subband images. Hence discrete wavelet transform (DWT) is a suitable tool to be used for designing pose invariant face recognition system. Another important issue in face recognition system is face localization. There are several methods for this task such as skin tone based face localization for face segmentation. Skin is

Probability Distribution Functions Based Face Recognition System Using Discrete Wavelet Subbands