This study presents an illumination pre-processing method termed as 'Discrete wavelet transform enhanced contrast limited adaptive histogram equalisation' (DWT E-CLAHE) to recognise the front view facial images in the difficult light conditions. A recent image enhancement method CLAHE-DWT motivates to combine the two-dimensional discrete wavelet transform (2D DWT) and CLAHE. The DWT E-CLAHE is implemented as follows: The original image is enhanced using the Gamma intensity correction (GIC); then split into low-frequency and high-frequency components using 2D DWT; finally, to the low-frequency components, the logarithmic transform, GIC and CLAHE are applied in the sequential order. The face recognition of DWT E-CLAHE is made using Gabor magnitude features. The face recognition of CLAHE-DWT is implemented for the first time. The experimental results of DWT E-CLAHE in the various face databases prove the following: (i) The proper selection of parameters of DWT E-CLAHE improves the brightness and contrast of the image. (ii) The recognition accuracy of DWT E-CLAHE in the Carnegie Mellon University (CMU) Pose, Illumination, and Expression (PIE) and Extended Yale B databases are extremely good, since the brightness and contrast are improved significantly. (iii) The performance comparison of DWT E-CLAHE outperforms CLAHE-DWT and state-of-the-art face recognition methods. (iv) DWT E-CLAHE recognises the varying facial expressions.

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