Illumination variation is one of the most significant factors affecting the performance of face recognition systems.As the adaptive smoothing estimation used in traditional Retinex algorithm can enhance the shadow edge falsely during smoothing face images

a novel Retinex algorithm based on adaptive smoothing with new a conduction function is proposed .This conduction function uses both spatial gradient and local inhomogeneity to measure the severity extent of pixel variation

which can smooth face images well without enhancing edge effects and lossing feature edges. During iterative process

the maximum between this iteration and last iteration is chosen to be the constraint

so this adaptive smoothing method with the proposed conduction function can be applied in Retinex theory to estimate illumination. Experimental results based on the Yale B face database show that the proposed algorithm can overcome shadows in side-illumination efficiently without losing feature edges of face images. The recognition rate is improved by 24.2% in the side-illumination condition at the best rate

and 4% in the no side-illumination condition compared with those of classical Retinex algorithms.The experimental results prove that the algorithm has illumination robustness

and can improve recognition rates under any illumination conditions effectively.