Simplified gabor wavelets for efficient feature extraction

Gabor wavelets (GWs) are commonly used for extracting local features for various applications like object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGWs for face recognition. Experimental results show that SGWs can achieve a performance level similar to GWs, while the runtime for feature extraction using SGWs is about 4.39 times faster than that of GWs, implemented by using fast Fourier transform (FFT).