The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

Template Matching Techniques in Computer Vision: Theory and Practice

Due to the robustness of Gabor features against local distortions caused by variance of illumination, expression and pose, they have been successfully applied for face recognition. The Facial Recognition Technology (FERET) evaluation and the recent Face Verification Competition (FVC2004) have seen the top performance of Gabor feature based methods. This paper aims to give a detailed survey of state of the art 2D face recognition algorithms using Gabor wavelets for feature extraction. Existing problems are covered and possible solutions are suggested.

A review on Gabor wavelets for face recognition

Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.

In this paper, we present a novel image classification system that is built around a core of trainable filter ensembles that we call Volterra kernel classifiers. Our system treats images as a collection of possibly overlapping patches and is composed of three components: (1) A scheme for a single patch classification that seeks a smooth, possibly nonlinear, functional mapping of the patches into a range space, where patches of the same class are close to one another, while patches from different classes are far apart-in the L_2 sense. This mapping is accomplished using trainable convolution filters (or Volterra kernels) where the convolution kernel can be of any shape or order. (2) Given a corpus of Volterra classifiers with various kernel orders and shapes for each patch, a boosting scheme for automatically selecting the best weighted combination of the classifiers to achieve higher per-patch classification rate. (3) A scheme for aggregating the classification information obtained for each patch via voting for the parent image classification. We demonstrate the effectiveness of the proposed technique using face recognition as an application area and provide extensive experiments on the Yale, CMU PIE, Extended Yale B, Multi-PIE, and MERL Dome benchmark face data sets. We call the Volterra kernel classifiers applied to face recognition Volterrafaces. We show that our technique, which falls into the broad class of embedding-based face image discrimination methods, consistently outperforms various state-of-the-art methods in the same category.

/pdf/trainable-convolution-filters-and-their-application-to-face-3ftwxgu8o9.pdf

Trainable Convolution Filters and Their Application to Face Recognition

An apparatus for and method of performing a most informative feature extraction (MIFE) method in which a facial image is separated into sub-regions, and each sub-region makes individual contribution for performing facial recognition. Specifically, each sub-region is subjected to a sub-region based adaptive gamma (SadaGamma) correction or sub-region based histogram equalization (SHE) in order to account for different illuminations and expressions. A set of reference images is also divided into sub-regions and subjected to the SadaGamma correction or SHE. A comparison is made between the each corrected sub-region and each corresponding sub-region of the reference images. Based upon the comparisons made individually for the sub-regions of the facial image, one of the stored reference images having the greatest correspondence is chosen. While usable individually, using the MIFE and/or SadaGamma correction or SHE together achieves a lower error ratio in face recognition under different expressions, illuminations and occlusions.

Apparatus for and method of feature extraction for image recognition

A recent trend in the field of pattern recognition has been the use of ensemble classifiers. If combined properly, the ensemble can achieve a higher identification rate than any individual classifier. Plurality voting is one of the most commonly used combination strategies. The performance of plurality voting can be improved if the decisions of different classifiers are weighted properly. In this paper, we both theoretically and experimentally analyze the performance of a weighted plurality voting combination strategy to combine the decisions of multiple classifiers. Theoretical expressions characterizing the performance of the weighted voting model are derived and the method is applied to the problem of human face recognition and voice recognition. The results show the advantage of employing weighted-voting-based ensemble classifiers in achieving high identification rates.

Weighted voting-based ensemble classifiers with application to human face recognition and voice recognition

In this paper we construct an associative memory model based on the restricted Coulomb energy (RCE) network. We propose a simple architecture and training algorithm for this RCE-based associative memory. We study the capacity of this memory model on the practical problem of human face recognition. In this case, capacity is described by two measures: the ability of the system to correctly identify known individuals, and the ability of the system to reject individuals who are not in the database. Experimental results are given which show how the performance of the system varies as the size of the database increases: up to 1000 individuals.

/pdf/an-rce-based-associative-memory-with-application-to-human-4dwe48ushb.pdf

An RCE-based associative memory with application to human face recognition

Gabor wavelet-based feature extraction has been emerging as one of the most promising ways to represent human face image data. In this paper, we examine the performance of two types of classifiers that can be used with Gabor features. In the first classifier, the distance between two images is computed by summing the local distances among all the nodes. In the second classifier, a voting strategy is used In addition, we examine two types of shift optimization procedures. The first is the standard elastic graph matching algorithm, and the second is a constrained version of the algorithm. Experimental results indicate that the voting-based classifier with constrained elastic graph matching gives improved results.

Combining Gabor features: summing vs. voting in human face recognition

In various studies, it has been demonstrated that combining the decisions of multiple classifiers can lead to better recognition result. Plurality voting is one of the most widely used combination strategies. In this paper, we both theoretically and experimentally analyze the performance of a plurality voting based ensemble classifier. Theoretical expressions for system performance are derived as a function of the model parameters: N (number of classifiers), m (number of classes), and p (probability that a single classifier is correct). Experimental results on the human face recognition problem show that the voting strategy can successfully achieve high detection and identification rates, and, simultaneously, low false acceptance rates.

Analysis of a Plurality Voting-based Combination of Classifiers

This paper describes a complete face recognition system. The system uses a template matching approach along with a training algorithm for tuning the performance of the system to solve two types of problems simultaneously: 1) correct classification experiments which correctly recognize and identify individuals who are in the database; and 2) false positive experiments which reject individuals who are not part of the database. Experimental results are given which indicate that this training method is capable of consistently producing high correct classification rates and low false positive rates.

Training algorithms for robust face recognition using a template-matching approach

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