Name
Title | ||
|---|---|---|
Spatially Coherent Feature Learning for Pose-Invariant Facial Expression Recognition. |
Abstract | ||
|---|---|---|
Feature learning has enjoyed much attention and achieved good performance in recent studies of image processing. Unlike the required training conditions often assumed there, far less labeled data is available for training emotion classification systems. In addition, current feature learning is typically performed on an entire face image without considering the dependency between features. These approaches ignore the fact that faces are structured and the neighboring features are dependent. Thus, the learned features lack the power to describe visually coherent facial images. Our method is therefore designed with the goal of simplifying the problem domain by removing expression-irrelevant factors from the input images, with a key region-based mechanism, which is an effort to reduce the amount of data required to effectively train the feature-learning methods. Meanwhile, we can construct geometric constraints between the key regions and its detected positions. To this end, we introduce a Spatially Coherent featurelearning method for Pose-invariant Facial Expression Recognition (SC-PFER). In our model, we first perform face frontalization through a 3D pose-normalization technique, which could normalize poses while preserving the identity information through synthesizing frontal faces for facial images with arbitrary views. Subsequently, we select a sequence of key regions around 51 key points in the synthetic frontal face images for efficient unsupervised feature learning. Finally, we introduce a linkage structure over the learning-based features and the corresponding geometry information of each key region to encode the dependencies of the regions. Our method, on the whole, does not require training multiple models for each specific pose and avoids separating training and parameter tuning for each pose. The proposed framework has been evaluated on two benchmark databases, BU-3DFE and SFEW, for pose-invariant Facial Expression Recognition (FER). The experimental results demonstrate that our algorithm outperforms current state-of-the-art FER methods. Specifically, our model achieves an improvement of 1.72% and 1.11% FER accuracy, on average, on BU-3DFE and SFEW, respectively.
|
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3176646 | TOMCCAP |
Keywords | Field | DocType |
Facial expression recognition, learning-based features, pose-invariant, spatially coherent feature learning | ENCODE,Normalization (statistics),Pattern recognition,Problem domain,Facial expression recognition,Computer science,Image processing,Computer network,Emotion classification,Invariant (mathematics),Artificial intelligence,Feature learning | Journal |
Volume | Issue | ISSN |
14 | 1s | 1551-6857 |
Citations | PageRank | References |
3 | 0.36 | 49 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feifei Zhang | 1 | 61 | 19.93 |
| Qirong Mao | 2 | 261 | 34.29 |
| xiangjun | 3 | 25 | 4.67 |
| Yongzhao Zhan | 4 | 344 | 51.09 |
| Ming Dong | 5 | 105 | 13.70 |