Face Recognition: A Survey

Abstract

Face recognition has gained a significant position among most commonly used applications of image processing furthermore availability of viable technologies in this field have contributed a great deal to it.

Study of Face Recognition Techniques: A Survey

“…In eigenface method, decoding is performed with the calculation of eigenvector and then it is represented as a matrix. However, Eigenface based face recognition systems is only suitable for images having the frontal faces but some researches identify a face with different poses have also been made [1]. Analyzing different results drawn from the researchers the accuracy ratio has been much improved in recent years as compared previous results.…”

“…One of the most viable feature of Neural Networks is it lessens the complexity. It learns from the training samples and then works fine on the images with changes in lighting conditions and increases accuracy [1]. The main drawback of the neural network is a more time is needed for its training.…”

“…However, Least Square Support Vector Machine (LS-SVM) [50], [51] is among the popular one in SVM types that is being successfully utilized for face recognition task. This provides advantage of fast computation, speed along with high recognition rate [1]. Component-based SVM classifier [16] is another variant of SVM in face recognition.…”

“…As a result this makes them illegible. Contrary to biological characteristics and traits of an individual person they cannot be stolen, forgotten or misplaced [1].…”

Mixtures of Trained Regression Curvesmodels for Handritten Arabic Character Recognition

“…Shape recognition has been the focus of many researchers for the past seven decades [1] and attracted many communities in the field of pattern recognition [2], artificial intelligence [3], signal processing [4], image analysis [5], and computer vision [6]. The difficulties arise when the shape under study exhibits a high degree in variation: as in handwritten characters [7], digits [8], face detection [9], and gesture authentication [10]. For a single data, shape variation is limited and cannot be captured ultimately due to the fact that single data does not provide sufficient information and knowledge about the data; therefore, multiple existence of data provides better understanding of shape analysis and manifested by mixture models [11].…”