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Handcraft and Learned Feature Extraction Techniques for Robust Face Recognition : A Review

机译:用于鲁棒人脸识别的手工和学习特征提取技术:综述

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In this paper, we will review face representation techniques that are used in face recognition process. There are two types of feature extraction: handcraft and learned features. PCA and LBP are handcraft feature extraction while the DeepFace, generating from convolutional neural network, is learned feature. PCA is an orthogonal transformation where a set of observations is converted to the principal components. The first few principal components have the largest variance hence represented images with small number of features. LBP is a local binary pattern which encodes local image into a binary pattern. LBP tolerates against changes in gray scale variations. By allowing the deep learning to automatically discover the image representations from raw data therefore DeepFace is a learned feature. In some cases, data may be unable to define specific feature especially for face representation. DeepFace is an alternative technique where features are generated through training/learning process without relying on specific algorithms. Learned features significantly outperform the handcraft one where the test set is unseen. PCA, LBP and DeepFace will be compared in terms of accuracy and computational time.
机译:在本文中,我们将回顾在人脸识别过程中使用的人脸表示技术。特征提取有两种类型:手工特征和学习特征。 PCA和LBP是手工特征提取,而从卷积神经网络生成的DeepFace是学习的特征。 PCA是正交变换,其中将一组观测值转换为主要成分。前几个主成分具有最大的方差,因此代表的图像具有少量特征。 LBP是一种本地二进制模式,它将本地图像编码为二进制模式。 LBP可以承受灰度变化的变化。通过允许深度学习自动从原始数据中发现图像表示,因此DeepFace是一种学习功能。在某些情况下,数据可能无法定义特定功能,尤其是对于面部表情。 DeepFace是一种替代技术,其中功能是通过训练/学习过程生成的,而无需依赖特定的算法。学到的功能大大优于看不见测试装置的手工功能。 PCA,LBP和DeepFace将在准确性和计算时间方面进行比较。

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