Abstract: Log-Gabor functions have extended tails at the high frequency end, can effectively improve the shortcoming of ordinary Gabor functions which would over-represent the low frequency components and under-represent the high frequency components, and Log-Gabor filter has no DC components, the bandwidth is not limited, so Log-Gabor filter is more suitable than Gabor filter to extract the face feature. After extracting features of a face image by a Log-Gabor filter bank which is composition of four scales and six orientations, the amount of data is 24 times of the original, but the embedded equipment resources are limited, it is difficult to deal with so many data. An embedded face recognition approach combined mutual information with Log-Gabor feature was proposed. A bank of Log-Gabor filters was applied on face images to extract features, mutual information was adopted to calculation weights, then the weights were used to fusion the Log-Gabor features. Two Dimensional PCA (2DPCA) was used to reduce dimensions. The nearest neighbor classifier was employed for classification. The experimental results show that the method can effectively reduce the recognition time in the case of keeping the recognition rate.

Key words: face recognition, Log-Gabor feature fusion, embedded, mutual information, 2DPCA