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首页> 外文期刊>International journal of parallel programming >Parallel Heat Kernel Volume Based Local Binary Pattern on Multi-Orientation Planes for Face Representation
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Parallel Heat Kernel Volume Based Local Binary Pattern on Multi-Orientation Planes for Face Representation

机译：多方向平面上基于并行热核体积的局部二值模式进行人脸表示

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摘要

Appropriate representation is one of the keys to successful face recognition technologies. Actual facial appearance sometimes differs dramatically because of variations in pose, illumination, expression, and occlusion. However, existing face representation methods remain insufficiently powerful and robust. Hence, we propose a new feature extraction approach for face representation based on heat kernel volume and local binary patterns. Multi-scale heat kernel faces are created in our proposed framework. We then reformulate these multi-scale heat kernel faces as three-dimensional volume. Furthermore, we generate multi-orientation planes from the heat kernel volume, which reflects orientation co-occurrence statistics among different heat kernel faces. Finally, we apply local binary pattern (LBP) analysis on the multi-orientation planes of the heat kernel volume to capture the microstructure and macrostructure of face appearance. Hence, we obtain the heat kernel volume based local binary pattern on multi-orientation planes (HKV–LBP–MOP) descriptor. The proposed method is successfully be paralleled. We applied the method to face recognition and obtain the performance of 99.28 and 87.82% on ORL and Yale databases respectively. Experimental results on the show that the proposed algorithm significantly outperforms other well-known approaches in terms of recognition rate.
机译：适当的表示方法是成功的面部识别技术的关键之一。实际的面部外观有时会因姿势，照明，表情和遮挡的变化而有很大差异。但是，现有的面部表示方法仍然不够强大和鲁棒。因此，我们提出了一种基于热核体积和局部二进制模式的人脸表征新特征提取方法。在我们提出的框架中创建了多尺度的热核面。然后，我们将这些多尺度的热核面重新构造为三维体积。此外，我们从热核体积生成多方向平面，这反映了不同热核面之间的方向共生统计。最后，我们在热核体积的多方向平面上应用局部二值模式（LBP）分析，以捕获面部外观的微观结构和宏观结构。因此，我们在多方向平面（HKV–LBP–MOP）描述符上获得了基于热核体积的局部二进制模式。所提出的方法已成功并行化。我们将该方法应用于人脸识别，在ORL和Yale数据库上的性能分别达到99.28％和87.82％。实验结果表明，该算法在识别率上明显优于其他知名方法。

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