End-face image analysis procedure for the calibration of optical fibre geometry test sets

Face image analysis, consisting of automatic investigation of images of (human) faces, is a hot research topic and a fruitful field. This introductory chapter discusses several aspects of the history and scope of face image analysis and provides an outline of research development publications of this domain. More prominently, different modules and some typical techniques for face image analysis are listed, explained, described, or summarized from a general technical point of view. One picture of the advancements and the front of this complex and prominent field is provided. Finally, several challenges and prominent development directions for the future are identified.

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NEU-FACES: A Neural Network-Based Face Image Analysis System

Adaptive and Natural Computing Algorithms - Lecture Notes in Computer Science ◽

10.1007/978-3-540-71629-7_51 ◽

2007 ◽

pp. 449-456 ◽

Cited By ~ 5

Author(s):

Ioanna-Ourania Stathopoulou ◽

George A. Tsihrintzis

Keyword(s):

Neural Network ◽

Image Analysis ◽

Face Image ◽

Image Analysis System ◽

Face Image Analysis ◽

Analysis System

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Comparing Ranking Methods for Tensor Components in Multilinear and Concurrent Subspace Analysis with Applications in Face Images

International Journal of Image and Graphics ◽

10.1142/s0219467815500060 ◽

2015 ◽

Vol 15 (01) ◽

pp. 1550006 ◽

Cited By ~ 3

Author(s):

Tiene A. Filisbino ◽

Gilson A. Giraldi ◽

Carlos E. Thomaz

Keyword(s):

Principal Component Analysis ◽

Image Analysis ◽

Gait Recognition ◽

Principal Component ◽

Component Analysis ◽

Face Image ◽

Image Databases ◽

Subspace Analysis ◽

Raw Data ◽

Face Image Analysis

In the area of multi-dimensional image databases modeling, the multilinear principal component analysis (MPCA) and concurrent subspace analysis (CSA) approaches were independently proposed and applied for mining image databases. The former follows the classical principal component analysis (PCA) paradigm that centers the sample data before subspace learning. The CSA, on the other hand, performs the learning procedure using the raw data. Besides, the corresponding tensor components have been ranked in order to identify the principal tensor subspaces for separating sample groups for face image analysis and gait recognition. In this paper, we first demonstrate that if CSA receives centered input samples and we consider full projection matrices then the obtained solution is equal to the one generated by MPCA. Then, we consider the general problem of ranking tensor components. We examine the theoretical aspects of typical solutions in this field: (a) Estimating the covariance structure of the database; (b) Computing discriminant weights through separating hyperplanes; (c) Application of Fisher criterium. We discuss these solutions for tensor subspaces learned using centered data (MPCA) and raw data (CSA). In the experimental results we focus on tensor principal components selected by the mentioned techniques for face image analysis considering gender classification as well as reconstruction problems.

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