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Erschienen in: Neural Processing Letters 3/2019

19.06.2018

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

verfasst von: Daoxiang Zhou, Dan Yang, Xiaohong Zhang, Sheng Huang, Shu Feng

Erschienen in: Neural Processing Letters | Ausgabe 3/2019

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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.
Fußnoten
1
\({\mathcal {R}}\equiv conv(P(\cdot |z_1)\),\(P(\cdot |z_2)\),\(P(\cdot |z_3))\).
 
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.
 
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Metadaten
Titel
Discriminative Probabilistic Latent Semantic Analysis with Application to Single Sample Face Recognition
verfasst von
Daoxiang Zhou
Dan Yang
Xiaohong Zhang
Sheng Huang
Shu Feng
Publikationsdatum
19.06.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9852-2

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