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In this paper we propose a novel genetically inspired learning method for facial expression recognition (FER).
Chen and Huang [16] proposed a clustering-based feature extraction method for facial expression recognition.
In this paper we propose an efficient and robust method for facial landmark detection and tracking from video sequences.
The purpose of this article is to propose a new feature extraction method for facial expression recognition.
This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state.
In [36], a method for facial expression recognition with Non-negative Matrix Factorization (NMF) and PCA-NMF is presented, and their best recognition rate is 93.72%.
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The interpolated forehead and melolabial flaps are well-established methods for facial restoration, especially for the repair of nasal defects following excision of cutaneous malignancies.
Subspace methods for facial feature detection are also used in [9 11].
Extensive experiments carried on the extended Cohn-Kanade and the JAFFE databases show that the proposed proCMF model provides even better performance than state-of-the-art methods for facial expression recognition.
Tables 2 and 3 indicate the recognition accuracies, and they show the performance of the proposed method compared to some state-of-the-art methods: 3D LUT [20] and LSH-CORF [9] are the latest methods for facial expressions recognition; LBP-TOP [10] is a well-known and classical expression recognition method.
In particular, researchers have developed a computerized method for detecting facial features using three-dimensional facial imaging and computer-based dense-surface modeling (see figure 3).
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