We present a scalable stacking-based deep neural network(S-DNN) for facial expression recognition. The network is a congregate of basic learning models in series to synthesize a deepneuralnetwork with feedforward ne...
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ISBN:
(纸本)9781728118673
We present a scalable stacking-based deep neural network(S-DNN) for facial expression recognition. The network is a congregate of basic learning models in series to synthesize a deepneuralnetwork with feedforward network architecture. Thur, choosing trainable learning modules is the core to effectively build S-DNN in an end-to-end manner. Inspired by the manifold learning archetype, we implement a Patch Discriminative Analysis(PDA) as a basic learning model, followed by hashing and block histogram on the top, which sample image in a low discriminative space, and finding an efficient representation of the training data. As those self-learnable models trained, a low dimensional discriminative feature is implicitly learned, which proves to be useful in facial expression recognition. Experimental results on the facial expression dataset(CK+) show that the proposed model is superior to its counterparts, capable of achieving state-of-the-art performance.
We present a scalable stacking-based deep neural network(S-DNN) for facial expression recognition. The network is a congregate of basic learning models in series to synthesize a deepneuralnetwork with feedforward ne...
详细信息
ISBN:
(纸本)9781728118680
We present a scalable stacking-based deep neural network(S-DNN) for facial expression recognition. The network is a congregate of basic learning models in series to synthesize a deepneuralnetwork with feedforward network architecture. Thur, choosing trainable learning modules is the core to effectively build S-DNN in an end-to-end manner. Inspired by the manifold learning archetype, we implement a Patch Discriminative Analysis(PDA) as a basic learning model, followed by hashing and block histogram on the top, which sample image in a low discriminative space, and finding an efficient representation of the training data. As those self-learnable models trained, a low dimensional discriminative feature is implicitly learned, which proves to be useful in facial expression recognition. Experimental results on the facial expression dataset(CK+) show that the proposed model is superior to its counterparts, capable of achieving state-of-the-art performance.
Touch-stroke dynamics is an emerging behavioral biometrics justified feasible for mobile identity management. A touch-stroke dynamics authentication system is composed of a hand-engineered feature extractor and a clas...
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Touch-stroke dynamics is an emerging behavioral biometrics justified feasible for mobile identity management. A touch-stroke dynamics authentication system is composed of a hand-engineered feature extractor and a classifier separately. In this letter, we propose a stacking-baseddeep learning network that performs feature extraction and classification, collectively dubbed Kernel deep Regression network (KDRN). The KDRN is built on multiple kernel ridge regressions (KRR) hierarchically, where each is trained analytically and independently. In principal, KDRN does not mean to learn directly from the raw touch-stroke data like other deep learning models, but it relearns from the pre-extracted features to yield a richer and a relatively more discriminative feature set. Subsequent to that, the authentication is carried out by KRR. Overall, KDRN achieves an equal error rate of 0.013% for intrasession authentication, 0.023% for intersession authentication, and 0.121% for interweek authentication on the Touchlaytics dataset.
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