基于Radon变换和离散小波变换的面部表情特征提取

H. Ali, V. Sritharan, M. Hariharan, S. K. Zaaba, M. Elshaikh
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引用次数: 7

摘要

提出了一种基于Radon和小波变换的人脸情感识别模式框架。Radon变换是平移和旋转不变量,因此它保留了像素强度的变化。本研究采用Radon变换将二维图像投影到Radon空间,然后进行离散小波变换(DWT)。在小波变换框架中,提取二级分解的近似系数(cA2)作为信息特征进行面部情绪识别。由于存在大量的系数,因此对提取的特征进行主成分分析(PCA)。采用k近邻分类器对七种面部情绪(愤怒、厌恶、恐惧、快乐、中性、悲伤和惊讶)进行分类。为了评估所提出方法的有效性,采用了JAFFE数据库。结果表明,该方法的识别率为91.3%,具有较好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature extraction using Radon transform and Discrete Wavelet Transform for facial emotion recognition
This paper presents a new pattern framework of using Radon and wavelet transform for facial emotion recognition. The Radon transform is translation and rotation invariants, hence it preserves the variations in pixel intensities. In this work, Radon transform has been used to project the 2D image into Radon space before subjected to Discrete Wavelet Transform (DWT). In DWT framework, the approximate coefficients (cA2) at second level decomposition are extracted and used as informative features to recognize the facial emotion. Since there are a large number of coefficients, hence the principal component analysis (PCA) is applied on the extracted features. The k-nearest neighbor classifier is adopted as classifier to classify seven (anger, disgust, fear, happiness, neutral, sadness and surprise) facial emotions. To evaluate the effectiveness of the proposed method, the JAFFE database has been employed. Based on the results obtained, the proposed method demonstrates the recognition rate of 91.3%, thus it is promising.
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