Abstract
This paper presents a novel and simple image feature extraction method, called exploring deep gradient information (DGI), for face recognition. DGI first captures the local structure of an image by computing the histogram of gradient orientation of each macro-pixel (local patch around the central pixel). One image can be decomposed into L sub-images (also called orientation images) according to the structure information of each macro-pixel since there are L bins in the local histogram. For each orientation image, dense scale invariant feature transform (DSIFT) is used to further explore the gradient orientation information. All DSIFT feature are concatenated into one augmented super-vector. Finally, dimensionality reduction technology is applied to obtain the low-dimensional and discriminative feature vector. We evaluated the proposed method on the real-world face image datasets NUST-RWFR, Pubfig and LFW. In all experiments, DGI achieves competitive results compared with state-of-the-art algorithms.
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Qian, J., Yang, J., Tai, Y. (2015). Exploring Deep Gradient Information for Face Recognition. In: Zha, H., Chen, X., Wang, L., Miao, Q. (eds) Computer Vision. CCCV 2015. Communications in Computer and Information Science, vol 547. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48570-5_16
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DOI: https://doi.org/10.1007/978-3-662-48570-5_16
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