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Analyzing hardware constraints of Gabor filtering operation for Facial Expression Recognition System

机译:分析面部表情识别系统Gabor滤波操作的硬件约束

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This paper presents hardware constraints analysis of Gabor filtering operation for its hardware implementation in a real time Facial Expression Recognition System (FERS). Gabor filter is the most common feature extractor employed for the realization of such system. Feature extraction using Gabor filter is efficient and has better discrimination capability. In this work, we have employed software-based approach to find the optimum filter and facial image size. These two factors employed in the Gabor filtering process directly affect the hardware resource utilization and hence we have considered these two factors for our analysis. We have used two versions of Gabor filter for feature extraction, one using the original Gabor filtering approach and the other its modified version using Image Pyramid based approach. Support Vector Machine (SVM) classifier has been used for analyzing the performance of the extracted feature.
机译:本文针对实时面部表情识别系统(FERS)中的硬件实现,介绍了Gabor滤波操作的硬件约束分析。 Gabor滤波器是用于实现该系统的最常见特征提取器。使用Gabor滤波器的特征提取效率很高,并且具有更好的识别能力。在这项工作中,我们采用了基于软件的方法来找到最佳的滤镜和面部图像尺寸。 Gabor过滤过程中使用的这两个因素直接影响硬件资源的利用率,因此我们在分析中已经考虑了这两个因素。我们使用了两种版本的Gabor滤波器进行特征提取,一种使用原始的Gabor滤波方法,另一种使用基于图像金字塔的方法进行修改。支持向量机(SVM)分类器已用于分析提取特征的性能。

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