Face recognition using decimated redundant discrete wavelet transforms

Abstract

As discrete wavelet transform (DWT) is sensitive to the translation/shift of input signals, its effectiveness could be lessened for face recognition, particularly when the face images are translated. To alleviate drawbacks resulted from this translation effect, we propose a decimated redundant DWT (DRDWT)-based face recognition method, where the decimation-based DWTs are performed on the original signal and its 1-stepshift, respectively. Even though the DRDWT realizes the decimation, it enables us to explore the translation invariant DWT representation for the periodic shifts of the probe image that is the most similar to the gallery images. Therefore, it can solve the problem of translation sensitivity of the original DWT and address the translation effect occurring between the probe image and the gallery image. To further improve the recognition performance, we combine the global wavelet features obtained from the entire face and the local wavelet features obtained from face patches to represent both holistic and detail facial features, apply separate classifiers to global and local features and combine the resulted global and local classifiers to form an ensemble classifier. Experimental results reported for the FERET and FRGCv2.0

Evaluating Feature Extractors and Dimension Reduction Methods for Near Infrared Face Recognition Systems

“…The third conclusion is that the deficiency of DWT to misalignments can be seen in this experiment. This finding corroborates the ideas of Li et al, [29] who mentioned misalignments as one of the deficiency of DWT.…”

“…This low performance is due to shift sensitivity of DWT. These findings further support the idea of the sensitivity of DWT to translation which was already highlighted in [29]. 7…”

Medical image illumination enhancement and sharpening by using stationary wavelet transform

“…Wavelets have been used quite frequently as tools in many image processing applications such as feature extraction [16], compression [17], [18], face recognition [19], [20], image super resolution [2], [21], [22], and medical image enhancement [23]. The decomposition of images into different frequency ranges permits the isolation of the frequency components into certain subbands [24]. Stationary Wavelet Transformation (SWT) is very similar to DWT where there exists no scaling function.…”