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A thorough review of the different approaches for facial animation can be found in [4].
More generally, unsupervised and semi-supervised approaches for facial feature extraction, event detection and classification have dragged interest [28].
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(3) An original approach for facial surface compression with the related reconstruction algorithm.
Fig. 1 Basic block diagram of the proposed approach for facial asymmetry analysis.
In this paper, we propose a group-aware deep feature learning (GA-DFL) approach for facial age estimation.
This work presents an illumination independent approach for facial expression recognition based on long wave infrared imagery.
The most common approach for facial landmark detection is cascaded regression, which is composed of two steps: feature extraction and facial shape regression.
Our main contributions are multifold: first, to the best of our knowledge, our CRCNN framework is the first comparative deep learning approach for facial age estimation and has demonstrated its outperformance over the state-of-the-art methods by experimenting with well-known face datasets.
Soyel et al. used the discriminative SIFT (D-SIFT) approach for optimal facial expression recognition, but this method is somewhat susceptible to the illumination variation [13].
Therefore, we studied the effects of mood on approach-avoidance tendencies for facial stimuli.
To the best of our knowledge, it is the first comparative approach in deep learning for facial age estimation and the experimental results validate the outperformance of our CRCNN approach over state-of-the-art methods.
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