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Discriminative Probabilistic Latent Semantic Analysis with Application to Single Sample Face Recognition

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Abstract

Face recognition is still a challenging issue due to the presence of intrinsic complexity, external variations and number limitation of training samples. In this paper, a novel face recognition method based on probabilistic latent semantic analysis (pLSA) model is developed, which mainly contains two stages: bag-of-words features extraction and semantic representation learning. In the first stage, to extract more structure information, the region-specific dictionary strategy is employed, i.e., generating a dictionary for each region. The encoded and sum-pooled features of all regions are concatenated together. In the second stage, a discriminative pLSA (DpLSA) model is presented, which initializes the word-topic distribution \(P(w|z_k)\) by the center point of the training data from category k. As a result, the problem of how to choose appropriate number of topics in classical topic model is alleviated, and the training process of DpLSA is very fast only requiring few iterations. Moreover, the discovered topic-document distribution \(P\left( z|d\right) \) is discriminative and semantic with the dominant topic entry corresponds to the category label of image d, which enables performing classification by \(P\left( z|d\right) \) directly. Extensive experiments on four representative databases demonstrate that the proposed DpLSA is effective for face recognition under single training sample and possesses a certain degree of robustness to illumination, pose, as well as occlusion.

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Notes

  1. \({\mathcal {R}}\equiv conv(P(\cdot |z_1)\),\(P(\cdot |z_2)\),\(P(\cdot |z_3))\).

  2. Code kindly provided at: http://www4.comp.polyu.edu.hk/~cslzhang/papers.htm.

  3. Code kindly provided at: http://www.cad.zju.edu.cn/home/dengcai/Data/Metric.html.

  4. Code kindly provided at: http://bmc.uestc.edu.cn/~fshen/.

  5. Code kindly provided at: http://mx.nthu.edu.tw/~tsunghan/Source%20codes.html.

  6. Following the original setting, whitening PCA (WPCA) is applied to reduce the dimension of PCANet features on FERET and LFW databases.

  7. Since the image size of FERET used in this paper is \(80 \times 80\) (not \(150 \times 90\)), so we fine-tune the model parameters of PCANet by varying \(k_1\) and \(k_2\) from 3 to 13 with step 2, hist block size from 6 to 20 with step 2, keeping \(L_1=L_2=8\). Then the optimal parameters are selected.

  8. Code kindly provided at: http://www.ifp.illinois.edu/~jyang29/ScSPM.htm.

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Acknowledgements

The authors would like to thank the anonymous reviewers whose valuable comments and suggestions greatly improve this paper. The work described in this paper was partially supported by the National Natural Science Foundation of China (Grant Nos. 61772093, 61402062, 61602068), Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT1196), Chongqing Research Program of Basic Science & Frontier Technology (Grant No. cstc2015jcyjA40037).

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Authors and Affiliations

  1. School of Software Engineering, Chongqing University, Chongqing, 401331, China

    Daoxiang Zhou, Dan Yang, Xiaohong Zhang & Sheng Huang

  2. College of Arts and Sciences, Shanxi Agricultural University, Shanxi, 030801, China

    Shu Feng

  3. Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education, Chongqing, 400044, China

    Xiaohong Zhang

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  1. Daoxiang Zhou
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Correspondence to Daoxiang Zhou.

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Zhou, D., Yang, D., Zhang, X. et al. Discriminative Probabilistic Latent Semantic Analysis with Application to Single Sample Face Recognition. Neural Process Lett 49, 1273–1298 (2019). https://doi.org/10.1007/s11063-018-9852-2

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