Abstract
It has been noticed that the performance of multi-ethnic facial expression recognition is affected by other-race effect significantly. Though this phenomenon has been noticed by psychologists and computer vision researchers for decades, the mechanism of other-race effect is still unknown and few work has been done to compensate or remove this effect. This work proposes an ICA-based method to eliminate the other-race effect in automatic 3D facial expression recognition. Firstly, the depth features are extracted from 3D local facial patches, and independent component analysis is applied to project the features into a subspace in which the projected features are mutually independent. The ethnic-related features and expression-related features are supposed to be separated in ICA subspace. Hence, ethnic-sensitive features are then determined by an entropy-based feature selection method and discarded to depress their influence on facial expression recognition. The proposed method is evaluated on benchmark BU-3DFE database, and the experimental results reveal that the influence caused by other-race effect can be suppressed effectively with the proposed method.
Highlights
Facial expressions play an important role in human nonverbal communication since they provide an effective way to express intentions and convey emotions
The proposed method is evaluated on the BU-3DFE database [24], which is originally collected for 3D facial expression recognition
The influence caused by other-race effect to facial expression recognition has been studied by psychologists for decades
Summary
Facial expressions play an important role in human nonverbal communication since they provide an effective way to express intentions and convey emotions. This paper proposes a ICA-based method to eliminate other-race effect from 3D facial expression recognition by removing the ethnic-related features from face images. Inspired by the method adopted in blind signal source separation, this work proposes an ICA-based other-race effect elimination method, which decomposes the mixed demographic information in human face by projecting face images to independent components. The experimental results show that the proposed method could ease the influence caused by other-race effect and improve the performance of multi-ethnic facial expression recognition. The influence to facial expression recognition caused by other-race effect could be eased if the ethnic-related features can be identified and removed. The training images could be projected into ICA subspace if the demixing matrix are obtained, and 3D facial features could be represented based on the independent components.
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