目前人脸识别的算法可以分类为： 基于人脸特征点的识别算法（Feature-based recognition algorithms）。 基于整幅人脸图像的识别算法（Appearance-based recognition algorithms）。
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Template-based recognition algorithms 基于模板的识别算法
Appearance-based recognition algorithms 基于整幅人脸图像的识别算法 ; 识别算法
Feature-based recognition algorithms 基于人脸特征点的识别算法
As the parametric representation of primitives is low dimensional, the integration of primitives and state-based recognition algorithms helps realize some more complex recognition models.
由于基元特征参数的维数低，因此，它与基于状态的各种识别方法相结合有利于实现更复杂识别方法。
Recognition algorithms for small moving target in strong noise based on single feature has a high false alarm. Sometimes the features of target and noise are very alike.
利用单个特征识别强噪声中的弱小运动目标，常因所提取的目标特征与噪声特征易混淆而导致高的虚警率。
On the basis of aforesaid work, the author further proposes robust face recognition algorithms based on sparse kernel auto - associative memory models.
在上述工作的基础上，本文主要研究了基于小世界体系的指数核自联想记忆模型在人脸识别中的应用。
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