To overcome the limitations of low efficiency of traditional face recognition, a novel method of face recognition based on Image Gradient Compensation pattern (IGC) is proposed. Firstly, gradient magnitude maps of a face image in four directions are calculated. Secondly, two gradient operators are produced by fusing the four gradients magnitude maps of a face image in multiple ways. Thirdly, the new gradient operators are used to compensate the original image and generate the IGC of the face image. Next, IGC feature maps are divided into several blocks, and the concatenated histogram calculated over all blocks is utilized as the feature descriptor of face recognition. Finally, Principal Component Analysis (PCA) is used to reduce the dimension of high-dimensional features. The recognition is performed by using the Support Vector Machine (SVM) classifier. Experimental results on YALE and CMU_PIE face databases validate that the algorithm in this study not only achieves high recognition rate, but also has excellent performance in computational efficiency.