Can PCA be used for face recognition?
PCA is a statistical approach used for reducing the number of variables in face recognition. In PCA, every image in the training set is represented as a linear combination of weighted eigenvectors called eigenfaces. These eigenvectors are obtained from covariance matrix of a training image set.
What is Eigenfaces in face recognition?
Eigenfaces is a method that is useful for face recognition and detection by determining the variance of faces in a collection of face images and use those variances to encode and decode a face in a machine learning way without the full information reducing computation and space complexity.
What is feature extraction in face recognition?
Abstract: Facial feature extraction is the process of extracting face component features like eyes, nose, mouth, etc from human face image. Facial feature extraction is very much important for the initialization of processing techniques like face tracking, facial expression recognition or face recognition.
Is SVM good for face recognition?
A SVM algorithm generates a decision surface separating the two classes. For face recognition, we re-interpret the deci- sion surface to produce a similarity metric between two facial images. This allows us to construct face-recognition algorithms.
Which classifier is best for face recognition?
Based on the results obtained, it is shown that ICA with the FLS-SVM classifier was the most effective, with a maximum recognition of 97.5 %.
Does principal component analysis (PCA) work for the recognition of faces?
In this paper, the performance of appearance-based statistical method called Principal Component Analysis (PCA) is tested for the recognition of face images. Principal Component Analysis is most successful technique to recognize faces. PCA performs well when the size of the database is small.
Do appearance-based statistical methods work for the recognition of colored face images?
In this paper, the performances of appearance-based statistical methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Independent Component Analysis (ICA) are tested and compared for the recognition of colored face images. Three sets of experiments are conducted for relative performance evaluations.
Can support vector machines (SVMs) be used for face recognition?
In this paper, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVMs are used to tackle the face recognition problem. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition.
What is the difference between PCA and ICA?
That is, PCA performance is better than LDA and ICA while performance of ICA performance is better than LDA on partial occlusions. The CSU Face Identification Evaluation System provides standard face recognition algorithms and standard statistical methods for comparing face recognition algorithms.