Facial Recognition understanding and Differences Between PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis)
نویسندگان
چکیده
منابع مشابه
Facial Expressions recognition Based on Principal Component Analysis (PCA)
The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expression to evaluate human character, judgment, feelings, and viewpoint The process of rrecognizing facial expression is a hard task due to the several circumstances such as facial ...
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The Linear Discriminant Analysis (LDA) has been widely used to derive the data-driven temporal filtering of speech feature vectors. In this paper, we proposed that the Principal Component Analysis (PCA) can also be used in the optimization process just as LDA to obtain the temporal filters, and detailed comparative analysis between these two approaches are presented and discussed. It’s found th...
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Principal Components Analysis (PCA) is an appearance based technique used widely for the dimensionality reduction and it records a great performance in face recognition. PCA based approaches typically include two phases: training and classification (Draper et al 2003). In the training phase, an Eigen space is established from the training samples using PCA and the training face images are mappe...
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Facial expression provides an important behavioural measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has recently become a promising research area. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this paper, we are evaluating the performance of PCA and LDA to recognize seven diffe...
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ژورنال
عنوان ژورنال: IJARCCE
سال: 2017
ISSN: 2278-1021
DOI: 10.17148/ijarcce.2017.63128