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首页> 外文会议>Human Vision and Electronic Imaging XII; Proceedings of SPIE-The International Society for Optical Engineering; vol.6492; Electronic Imaging Science and Technology >Hidden Markov Model-based face recognition using selective attention
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Hidden Markov Model-based face recognition using selective attention

机译：基于选择性注意的基于隐马尔可夫模型的人脸识别

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摘要

Sequential methods for face recognition rely on the analysis of local facial features in a sequential manner, typically with a raster scan. However, the distribution of discriminative information is not unifom over the facial surface. For instance, the eyes and the mouth are more informative than the cheek. We propose an extension to the sequential approach, where we take into account local feature saliency, and replace the raster scan with a guided scan that mimicks the scanpath of the human eye. The selective attention mechanism that guides the human eye operates by coarsely detecting salient locations, and directing more resources (the fovea) at interesting or informative parts. We simulate this idea by employing a computationally cheap saliency scheme, based on Gabor wavelet filters. Hidden Markov models are used for classification, and the observations, I.e. features obtained with the simulation of the scanpath, are modeled with Gaussian distributions at each state of the model. We show that by visiting important locations first, our method is able to reach high accuracy with much shorter feature sequences. We compare several features in observation sequences, among which DCT coefficients result in the highest accuracy.
机译：用于面部识别的顺序方法依赖于顺序进行的局部面部特征分析，通常使用光栅扫描。然而，区别信息在面部表面的分布并非统一。例如，眼睛和嘴巴比脸颊信息丰富。我们提出了一种顺序方法的扩展，其中考虑了局部特征的显着性，并用模仿人眼扫描路径的引导扫描代替了光栅扫描。引导人眼的选择性注意机制通过粗略地检测显着位置，并将更多的资源（中央凹）引导到有趣或有意义的部分来进行操作。我们通过基于Gabor小波滤波器的廉价计算显着性方案来模拟此想法。隐藏的马尔可夫模型用于分类，而观测值即通过在模型的每个状态下的高斯分布对通过扫描路径的仿真获得的特征进行建模。我们表明，通过首先访问重要位置，我们的方法能够以更短的特征序列达到高精度。我们比较了观察序列中的几个特征，其中DCT系数导致了最高的准确性。

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