To improve the recognition rate and reduce the false-recognition rate in real-time detection of flame in video surveillance, a fast flame recognition algorithm based on multi-feature logarithm regression model was proposed. Firstly, the image was segmented according to the chromaticity of the flame, and the Candidate Fire Region (CFR) was obtained by subtracting the moving target image with reference image. Secondly the features of the CRF such as area change rate, circularity, number of sharp corners and centroid displacement were extracted to establish the logarithmic regression model. Then, a total of 300 images including flame and non-flame images, which were got from National Institute of Standards and Technology (NIST), Computer Vision laboratory of Inha University (ICV), Fire detection based on computer Vision (VisiFire) and the experimental library consisting of the candle and paper combustion were used to parametric learning. Finally, 8 video clips including 11071 images were used to validate the proposed algorithm. The experimental results show that the True Positive Rate (TPR) and True Negative Rate (TNR) of the proposed algorithm are 93% and 98% respectively. The average time of identification is 0.058 s/frame. Because of its fast identification and high recognition rate, the proposed algorithm can be applied in embedded real-time flame image recognition.